ASA Research Using Excel to Detect Fraud J. Carlton Collins, CPA ASA Research - Atlanta, Georgia 770.842.5902 Carlton@ASAResearch.com Using Excel to Detect Fraud Table of Contents 1. Random Numbers – Attendees will learn how to generate random numbers to be used for statistical sampling purposes. ................................................................................................ 5 2. Consolidating Data – Attendees will learn how to consolidate data (such as budgets, financial reports, departmental reports, inventory lists, salesperson reports, location reports, etc.) using four different techniques as follows: Formulas, spearing formulas, consolidation tools, pivot tools. ................................................................................................................................ 9 3. Benford’s Law – Benford’s Law predicts the occurrence of digits in large sets of data and these predictions might help red-flag potential irregularities. ........................................................ 23 4. Using Regression to Create & Using Budgets to Detect and Prevent Fraud – An accurate budget should be the CPAs’ first line of defense for detecting and preventing fraud. Attendees will learn how to quickly create a budget using regression analysis applied to historical data. You will also learn how to scrutinize each line item using a variety of techniques including Pearson, R-Square, Skew and Kurtosis functions to determine if a suitable basis for regression analysis exists, and if not, alternative methods will be used to budget that particular line item. That budget will then be seasonalized and rounded. From there, a balance sheet budget and cash flow forecast will be prepared based on the seasonalized budget, and the importance of using a seasonalized budget to detect and prevent fraud will be discussed. ..................... 29 5. Profit Margin Monitoring – Profit margins that miss their target speak volumes. Attendees will learn how to budget for profit margins by asking two simple questions and working backwards using prior year data to target specific profit margins. Once established, these profit margins can also be used as benchmarks to help detect fraud or errors. .................. 49 6. Proof of Cash - Many auditors use a four-column bank reconciliation, also known as a Proof of Cash, to help shed light on error, misstatements, and fraud............................................. 54 7. Excel Data Cleaning – Attendees will learn how to clean data so that Excel’s tools can be applied to analyze the data. For example, a general ledger will be exported to Excel and the steps necessary to prepare the data for analysis will be explained. Attendees will learn how to parse data using functions as well as the Text to Columns tool, and will learn when the functions work better method for parsing data. .................................................................... 58 8. Data Cleaning Case Study - Preparing QuickBooks Data - When it comes to pivoting QuickBooks data in Excel, you must first do a little bit of cleanup work before pivoting process can begin.. ............................................................................................................................... 75 www.CarltonCollins.com Page 2 Copyright April 2013 Using Excel to Detect Fraud 9. Looking for Fraud – This section coves 28 common things to look for when examining for fraud, and suggests various Excel tools that might help the fraud examiner conduct these examinations. .......................................................................................................................... 80 10. Data Analysis Tools – Attendees will practice using data analysis tools to slice and dice data, filter data, group data, subtotal data, and pivot data. These topics focus on the most important aspect of Excel – the data tools – which can be specifically used to analyze data and detect anomalies. ........................................................................................................... 85 11. Querying – Attendees will learn how to pull data directly from an accounting system, from within Excel, for quick and easy data analysis. .................................................................... 111 12. Sparklines - Sparklines are new tools in Excel that can be used to visually analyze large volumes of data, and attendees will learn how to utilize this tool to save time and provide better data visualizations. ................................................................................................... 117 13. Conditional Formatting – Attendees will practice with Excel’s conditional formatting tools which allows the user to create rules for highlighting data with different colors to help visually analyze the data. .................................................................................................................. 117 14. Excel Functions – Attendees will also learn about a variety of tips and tricks such as Aggregate function which can be a useful tool in analyzing data, and will receive a list of the top 171 functions the course instructor thinks apply to CPAs. ......................................................... 121 15. Ratio Reporting – Attendees will receive sample workbooks and templates containing sample functions and ratio calculations. .............. Visit www.CarltonCollins.com, click the Fraud tab 16. Instructor Biography ............................................................................................................ 144 www.CarltonCollins.com Page 3 Copyright April 2013 Using Excel to Detect Fraud Using Excel to Detect Fraud CPE Course Information Learning Objectives Course Level Pre-Requisites Advanced Preparation Presentation Method Recommended CPE Credit Handouts Instructors To increase the productivity of accountants and CPAs using Excel’s commands and functions related to possibly detecting fraud Intermediate Familiarity with Microsoft Excel None Live lecture using full color projection systems and live Internet access with follow up course materials 8 hours Templates, checklists, web examples, manual J. Carlton Collins, CPA AdvisorCPE is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be addressed to the national Registry of CPE Sponsors, 150 Fourth Avenue, Nashville, TN, 37219-2417. Telephone: 615-880-4200. Copyright April 2013, ASA Research and Accounting Software Advisor, LLC 4480 Missendell Lane, Norcross, Georgia 30092 770.842.5904 All rights reserved. No part of this publication may be reproduced or transmitted in any form without the express written consent of ASA Research, subsidiaries of Accounting Software Advisor, LLC. Request may be e-mailed to marylou@asaresearch.com or further information can be obtained by calling 770.842.5904 or by accessing the ASAResearch home page at: http://www.ASAResearch.com/ All trade names and trademarks used in these materials are the property of their respective manufacturers and/or owners. The use of trade names and trademarks used in these materials are not intended to convey endorsement of any other affiliations with these materials. Any abbreviations used herein are solely for the reader’s convenience and are not intended to compromise any trademarks. Some of the features discussed within this manual apply only to certain versions of Excel, and from time to time, Microsoft might remove some functionality. Microsoft Excel is known to contain numerous software bugs which may prevent the successful use of some features in some cases. Accounting Software Advisor makes no representations or warranty with respect to the contents of these materials and disclaims any implied warranties of merchantability of fitness for any particular use. The contents of these materials are subject to change without notice. Contact Information for J. Carlton Collins Carlton@ASAResearch.com www.Facebook.com/CarltonCollins www.CarltonCollins.com Page 4 Copyright April 2013 Using Excel to Detect Fraud Random Numbers Excel provides two tools for generating random numbers as follows: 1. RAND 2. RANDBETWEEN (You must first activate the Analysis ToolPak) RAND - The RAND function in Microsoft Excel allows you to generate random numbers in Excel. Specifically, type RAND() in a given cell to produce a random number between 0 and 1, as shown. Comments 1. RAND is a volatile function, which means it will be recalculated any time the enter key is pressed, so the random number constantly changes. To prevent random numbers from changing, most people copy and paste them as values. 2. Excel’s RAND function can be used to generate random numbers from the Uniform distribution, however, be aware that prior to Excel 2003, this function should not be used with large simulation models because the older versions of Excel use the generation algorithm which has a relatively small period (less than 1 million numbers), so if your model contains hundreds of variables and you are running the simulation tens of thousands of times, you can run out of random numbers. This problem has been fixed in Excel 2003 and later versions. 3. Note that there is a known bug in Excel 2003 causing the RAND function to return negative numbers, which can be negated using the ABS (absolute function). www.CarltonCollins.com Page 5 Copyright April 2013 Using Excel to Detect Fraud RANDBETWEEN - In Excel 2010, 2007 and 2003, you must first activate Analysis Tool Pack add-in as follows. In Excel 2010 and 2007, select File, Options, Add-ins, GO and place a check in the check box labeled Data Analysis ToolPak, then click OK. In Excel 2003, select Tools, Add ins,… The RANDBETWEEN returns a random integer between two specified numbers, as shown. Suppose you wanted to select 100 random numbers from three different ranges of the population. There are several approaches you might take. The first approach might be simply to list all possible values, then use RAND to create a random number adjacent to those values, and then sort. The screen shots below show the data before and after sorting. Once sorted, simply take the desired number of samples from the top of the randomly sorted list, as suggested by the top 7 values shaded in the second screen shot. www.CarltonCollins.com Page 6 Copyright April 2013 Using Excel to Detect Fraud This method takes up a lot of Excel screen real estate, but so what? Excel has millions of rows and the generation of such a report is fairly straight forward and fast. Just use the FILL command to fill in the necessary ranges, then add RAND and sort – it might take you 2 to 3 minutes. Random Numbers Given Three Ranges of Population Data Another way to generate random numbers is to use RANDBETWEEN, and assigning the probability that a random number is selected from a given range based upon the percentage that range represents compared to the total population. For example, consider the following example: www.CarltonCollins.com Page 7 Copyright April 2013 Using Excel to Detect Fraud Cells C4 through E5 contain the low and high values for three separate ranges of data making up the total population. We start by calculating the number of occurrences in each range (1,111, 1,277 and 55 in this example), and then calculate the percentage each range represents for the total population (45%, 52% and 2% in this example). Thereafter, a series of RANDBETWEEN functions are used to produce random number triggers between 1% and 100% (contained in cells I3 through I22 in this example), and then an IF function is used to calculate within which range each random number trigger falls. For example, the first random number trigger is 49% (in cell I3), which falls within the second range data. The RANDBETWEEN function in cell J3 thusly calculates a random number using the second range of data’s low and high values (6,500 and 7,777 in this example), and the first random number generated is 7,731 in this example. This method could theoretically be used to calculate random numbers for many separate ranges of data, with each member of the population having an equal chance to be selected. Download this workbook at www.CarltonCollins.com/5random.xlsx. www.CarltonCollins.com Page 8 Copyright April 2013 Using Excel to Detect Fraud Consolidating Data in Excel Consolidating data is a common task for CPAs, and Excel offers a variety of methods for performing this task. The particular method you use will probably depend on the layout of your data, and you may need to clean, edit or manipulate your data a bit to prepare it for consolidation. CPAs often have a need to consolidate data such as budgets, months, departments, locations, warehouses and sale representatives. Following I will explain four different consolidation methods - two methods for consolidating data with similar layouts, and two more methods for consolidating data with dis-similar layouts. These four methods are as follows: A. B. C. D. Simple formulas. Spearing formulas. The “Data Consolidate Command”. The “PivotTable Wizard”. A. Using Simple Formulas To Consolidate Similar Data The workbook below contains budgets with identical layouts for Departments A, B, C and D. The goal is to consolidate these four budgets into a single consolidated budget. www.CarltonCollins.com Page 9 Copyright April 2013 Using Excel to Detect Fraud 1. Insert a New Worksheet on which the consolidation will appear i. Don’t click the New Sheet or Add Sheet Option - Because there is a better and quicker approach. ii. CTRL + Drag Tab – To insert a new worksheet, select worksheet labeled “Dept D”; then use the CTRL + Drag Tab keystroke combination to create a duplicate worksheet of Dept D. The advantage is that the data, column widths, page footers and headers, and margin settings are all duplicated automatically, so you don’t have to create a new page from scratch. iii. (The menu method for achieving this same procedure is to right click on the tab and select Move or Copy, but CTRL + Drag Tab is quicker.) iv. Clean the Page – Clean the new worksheet by deleting the data in the grid area, so that new formulas can be inserted. v. Re-label – Change the worksheet label in Cell A1 and on the Worksheet Tab to read “consolidated”. www.CarltonCollins.com Page 10 Copyright April 2013 Using Excel to Detect Fraud 2. Formula – In cell B5, enter a formula adding the B5 cells in the four budget sheets. The formula should look like this: ='Dept A'!B5+'Dept B'!B5+'Dept C'!B5+'Dept D'!B5 3. Copy – Copy the formula down and across to complete the consolidation, and you are done. www.CarltonCollins.com Page 11 Copyright April 2013 Using Excel to Detect Fraud B. Using Spearing Formulas To Consolidate Similar Data The workbook below contains budgets with identical layouts for Departments A, B, C and D. The goal is to consolidate these four budgets into a single consolidated budget. 1. Insert a New Worksheet on which the consolidation will appear i. Don’t click the New Sheet or Add Sheet Option - Because there is a better and quicker approach. ii. CTRL + Drag Tab – To insert a new worksheet, select worksheet labeled “Dept D”; then use the CTRL + Drag Tab keystroke combination to create a duplicate worksheet of Dept D. The advantage is that the data, column widths, page footers and headers, and margin settings are all duplicated automatically, so you don’t have to create a new page from scratch. www.CarltonCollins.com Page 12 Copyright April 2013 Using Excel to Detect Fraud iii. (The menu method for achieving this same procedure is to right click on the tab and select Move or Copy, but CTRL + Drag Tab is quicker.) iv. Clean the Page – Clean the new worksheet by deleting the data in the grid area, so that new formulas can be inserted. v. Re-label – Change the worksheet label in Cell A1 and on the Worksheet Tab to read “consolidated”. 2. Formula – In cell B5, enter a formula adding the B5 cells in the four budget sheets. The formula should look like this: =SUM('Dept A:Dept D'!C5) I use the mouse to accomplish this step. Start by typing “=SUM(“, then click on cell B5 in Dept A, hold the shift key down, and click cell B5 in Dept D. www.CarltonCollins.com Page 13 Copyright April 2013 Using Excel to Detect Fraud 3. Copy – Copy the formula down and across to complete the consolidation, and you are done. www.CarltonCollins.com Page 14 Copyright April 2013 Using Excel to Detect Fraud C. Using the “Data Consolidate Command” To Consolidate Dissimilar Similar Data The workbook below contains dis-similar budgets for Departments A, B, C and D. Specifically, the four worksheets contain budgets for separate departments, and some budgets contain more rows and different row descriptions than others. The goal is to consolidate these four departmental budgets using the Consolidate Command. 1. Create A New Worksheet – Insert a new worksheet. Because there is no need to duplicate a template from another worksheet you might be tempted to use the New Sheet button this time; however, this approach does not duplicate the worksheet’s page footers, headers or margin settings. Therefore I would still recommend that you use the CTRL + Drag Tab method as described in examples 1 & 2 above to create a new sheet, and I would then eliminate the data on the new sheet by deleting those columns that contain data. 2. Label – As before, label the new worksheet in Cell A1 and on the Worksheet Tab to read “Consolidated”. 3. Select Cell – Select a blank cell on the new worksheet such as B3. 4. Use the Consolidate Command - From the Data tab, select Consolidate to display the Consolidate dialog box as pictured below. Click the Cell Chooser button, then highlight the data only on Dept A, click Enter, and then click Add. Repeat this process for Dept B, Dept C and Dept D. www.CarltonCollins.com Page 15 Copyright April 2013 Using Excel to Detect Fraud 5. Check the check boxes to use Labels in the Top Row and Left Column. 6. Finish – Click OK to produce the results. www.CarltonCollins.com Page 16 Copyright April 2013 Using Excel to Detect Fraud 7. Add Totals and Formatting - Highlight your data and expand the selection to include a blank bottom row and blank right column. Click the AutoSum tool, add formatting and you are done. Comments: Row Descriptions - Note that the consolidation works only to the extent that the different worksheets contain the same row descriptions. If your four department heads had used the descriptions: Rent, Rent Exp., Rent Expense, and Rental Expense, then those rows would not actually be consolidated, rather they would be shown as four separate rows in the resulting consolidation. However, because all four department heads did use the phrase Rent to describe that row, the four respective rent rows were properly consolidated. Account Numbers – As an option, you might insert account numbers to the left of the row descriptions to consolidate dissimilar information which contains dis-similar row descriptions. To Update – To update the results, place your cursor in the upper left hand corner of the Consolidation range, and rerun the Consolidate command. If the resulting report is a different size, you may need to clean up your data and reapply totals. www.CarltonCollins.com Page 17 Copyright April 2013 Using Excel to Detect Fraud Consolidate Different Workbooks – Excel can also consolidate data from different workbooks. The procedure is exactly the same except that you use the Browse button instead of the Cell Chooser button to point to your data ranges. The Problem with Data Consolidate The problem with Data Consolidate occurs when you attempt to change the source data, for example when you insert a new row in Dept A; a comedy of errors ensues as follows: 1. When you change the source data, the consolidated report does not update automatically. 2. Pressing the Refresh button does nothing. 3. To update the consolidated report, you must rerun the data consolidate command. But upon rerunning this command, you find that your cursor needs to be in the exact same location when you ran it last time. 4. You also find that you need to erase the previous data before re-running the Data Consolidate command. 5. Next you find that in Excel 2003, 2007 and 2010, you need to re-adjust your consolidating range for Dept A because the tool did not automatically expand the selection when the row was inserted in Dept A. (This issue has been corrected in Excel 2013.) Because source data tends to change frequently, you are probably better off using the next method to consolidate your data - the PivotTable and Chart Wizard method. www.CarltonCollins.com Page 18 Copyright April 2013 Using Excel to Detect Fraud D. Using A PivotTable To Consolidate Dissimilar Similar Data The workbook below contains the same dis-similar budget data used in example 3 above. The four worksheets contain budgets for separate departments, and some budgets contain more rows and different row descriptions than others. The goal is to consolidate these four departmental budgets using the PivotTable approach. 1. Create A New Worksheet – Start by inserting a new worksheet. (Because there is no need to duplicate a template from another worksheet you might be tempted to use the New Sheet button this time; however, this approach does not duplicate the worksheet’s page footers, headers or margin settings. Therefore I would still recommend that you use the CTRL + Drag Tab method as described in examples 1 & 2 above to create a new sheet, and I would then eliminate the data on the new sheet by deleting those columns that contain data.) 2. Label – Label the new worksheet in Cell A1 and on the Worksheet Tab to read Consolidated. 3. Select Cell – Select a blank cell such as B3. 4. Add the PivotTable Wizard to Your Quick Access Toolbar – The PivotTable Wizard in Excel 2003 allows you to pivot multiple consolidation ranges, but for unknown reason this tool is hidden in later versions of excel. Therefore, in Excel 2013, 2010 and 2007, you must first customize your Quick Access toolbar and insert the icon titled PivotTable and PivotChart Wizard as shown below. www.CarltonCollins.com Page 19 Copyright April 2013 Using Excel to Detect Fraud To add this tool, right-click your Quick-Access toolbar, select Customize Quick Access Toolbar, select the option to View All Commands, locate the PivotTable and PivotChart Wizard icon and add it to your toolbar. The resulting toolbar will appear as follows. . 5. Run the PivotTable Wizard – Click the PivotTable and PivotChart Wizard icon to display the PivotTable and PivotChart Wizard dialog box as shown below. Choose the Multiple consolidation ranges option and click Next, and Next again. The dialog box on the right should now be displayed. Click the Cell Chooser button, then highlight the data only on Dept A, click “Enter”, and then click “Add”. Repeat this process for Dept B, C and D until the PivotTable Wizard appears as follows. www.CarltonCollins.com Page 20 Copyright April 2013 Using Excel to Detect Fraud 6. Finish – Click “FINISH” to produce the results. 7. Add Formatting - Highlight your data and add comma formatting with no decimals, adjust the columns widths to taste, center the column headings; then you are done. www.CarltonCollins.com Page 21 Copyright April 2013 Using Excel to Detect Fraud Comments: The PivotTable Wizard approach is superior to the Data Consolidate approach for many reasons as follows: 1. Totals are automatic inserted with the PivotTable method. 2. Total column and row labels are automatically inserted with the PivotTable method. 3. AutoFilter buttons are automatic inserted with the PivotTable method. 4. If the source data changes, such as inserting a new row in Dept A, simply click refresh to update. 5. The resulting PivotTable is drillable. 6. The resulting PivotTable can be pivoted. 7. The PivotTable report offers many PivotTable tools such as PivotTable formatting which Data Consolidate does not offer. Download these Consolidation example templates at: www.CarltonCollins.com/consolidatesimilarbudgets.xlsx www.CarltonCollins.com/consolidatedissimilarbudgets.xlsx www.CarltonCollins.com Page 22 Copyright April 2013 Using Excel to Detect Fraud Benford’s Law Benford’s Law predicts the occurrence of digits in large sets of data. Simply put, this law maintains that we can expect some digits to occur more often than others. For example, the numeral 1 should occur as the first digit in any multiple-digit number about 30.1% of the time, while the numeral 9 should occur as the first digit only 4.6% of the time. We also can apply the law to determine the expected occurrence of the second digit of a number, the first two digits of a number and other combinations. How can such predictions help you red-flag potential irregularities? When someone creates false transactions or commits a data-entry error, the resulting numbers often deviate from the law’s expectations. This is true when someone creates random numbers or intentionally keeps certain transactions below required authorization levels. For example, in 2008, Bernie Madoff famously created fictitious data to hide an estimated $65 billion in losses resulting from Madoff’s investment Ponzi scheme. – Had the CPAs looked at this data using Benford’s Law, they might have found that the digits smelled of fraud, perhaps triggering a deeper investigation. Applying Benford’s Law Using Excel According to Benford’s Law, the various digits should occur as the first digit position according to the following percentages. www.CarltonCollins.com Page 23 Copyright April 2013 Using Excel to Detect Fraud To analyze data, simply use the LEFT function to extract the leading digits, and then add them up as follows. As a simple example, I found a random workbook containing 136 rows of revenue amounts. I entered a formula in cell C4 to extract the first digit and copied this formula down. Next I used the COUNTIF function to count the number of occurrences of each of the nine digits, and calculated their rate of occurrence, then charted the results. You can clearly see that this data pattern does not conform to Benford’s law, and yes, I fabricated this particular set of data years ago. When Excel helps you spot a deviation like this, it raises a red flag. Considerable statistical research supports the effectiveness of Benford’s Law, making it a valuable tool for CPAs. The technique isn’t guaranteed to detect fraud in all situations but is useful in analyzing the credibility of accounting records. www.CarltonCollins.com Page 24 Copyright April 2013 Using Excel to Detect Fraud A Note of Caution Benford’s Law is not effective for all financial data. If the data set is small, the law becomes less accurate because there are not enough items in the sample and so the rules of randomness don’t apply—or at least apply with less predictability. Also, if the data include built-in minimums and maximums, they also might not conform well to the law’s predictions. For example, consider a petty-cash fund where all disbursements are between a $10 minimum and a $20 maximum. All first digits would be either 1 or 2, and the expected distribution of first digits would not apply. Likewise, when a company’s major product sells for, say, $9.95, most sales totals will be a multiple of 995, again offsetting the value of the process. Finally, when a data set consists of assigned numbers, such as a series of internally generated invoice numbers, the data will not follow a Benford distribution. History of Beford’s Law Newcomb, 1881 - The discovery of Benford's law dates back to 1881, when the American astronomer Simon Newcomb noticed that in logarithm tables (used at that time to perform calculations) the earlier pages (which contained numbers that started with 1) were much more worn than the other pages. Newcomb's published result is the first known instance of this observation and includes a distribution on the second digit, as well. Newcomb proposed a law that the probability of a single number N being the first digit of a number was equal to log(N + 1) − log(N). Benford, 1938 - The phenomenon was again noted in 1938 by the physicist Frank Benford, who tested it on data from 20 different domains and was credited for it. His data set included the surface areas of 335 rivers, the sizes of 3259 US populations, 104 physical constants, 1800 molecular weights, 5000 entries from a mathematical handbook, 308 numbers contained in an issue of Readers' Digest, the street addresses of the first 342 persons listed in American Men of Science and 418 death rates. The total number of observations used in the paper was 20,229. This discovery was later named after Benford. Varian, 1972 - In 1972, Hal Varian suggested that the law could be used to detect possible fraud in lists of socio-economic data submitted in support of public planning decisions. Based on the plausible assumption that people who make up figures tend to distribute their digits fairly uniformly, a simple comparison of first-digit frequency distribution from the data with the expected distribution according to Benford's law ought to show up any anomalous results. Nigrini, 1999 - Following this idea, Mark Nigrini showed that Benford's law could be used in forensic accounting and auditing as an indicator of accounting and expenses fraud. In practice, applications of Benford's law for fraud detection routinely use more than the first digit. www.CarltonCollins.com Page 25 Copyright April 2013 Using Excel to Detect Fraud Analyzing All Digits Building on the simple example described above, we can expand our Excel formulas to analyze all of the digits included in a set of data as follows: In the example shown below, I have used the MID function to extract each digit from the column of values in column A, and the resulting individual numerals are displayed in columns C through H. Next, I totaled the occurrence for each number 1 through 9 in the summary box and charted the results. In this example, the data does appear to ever so slightly adhere to Benford’s Law as the first 4 bars in the chart and a few others seem to come close to matching Benford’s declining curve. According to Benford’s Law one would expect lower numerals to appear more frequently than higher values, but why? Lower digits (1, 2,& 3) tend to appear more frequently than higher digits (7, 8, & 9) because it is easier to own 1 acre than 9 acres; and more people have $100 than $900. Lower numbers are typically more achievable than higher numbers in many situations. For this www.CarltonCollins.com Page 26 Copyright April 2013 Using Excel to Detect Fraud reason, we would expect to see an analysis of numbers form a slightly curved declining chart like this one: However, the presence of the leading digit may significantly skew the data, therefore, we could ignore the leading digit and analyze the occurrence of the remaining 8 numerals in an effort to determine whether or not the data appears to roughly follow Benford’s Law. Ignoring the leading digit reveals the following analysis. www.CarltonCollins.com Page 27 Copyright April 2013 Using Excel to Detect Fraud As expected, the numeral 1 occurs less often than when the leading digits were included. Still, with the leading digit ignored, the data appears not to follow Benford’s Law. Conclusion This case study was intentionally brief and is only intended to convey the general ideas related to Benford’s law. The forensic CPA may choose to run these numbers to help confirm suspicions or beliefs related to the authenticity of a large data set of numbers in question. There are probably many instances where this approach would offer little value, however in a high ticket audit with high potential for fraud, running Benford’s Law analysis is a rather quick exercise which may offer insights to help the forensic CPA determine how to best proceed. www.CarltonCollins.com Page 28 Copyright April 2013 Using Excel to Detect Fraud Using Regression to Create Budgets & Using Budgets to Detect and Prevent Fraud Excel provides the ability to extrapolate data from your accounting system to produce budgets, projections or forecasts using the least squares method of linear regression. The process is extremely easy as illustrated in the following example. A Quick Example: In this example I have exported the income statements for the past six years from my QuickBooks accounting system. The next step is to highlight these five columns (from 2009 through 2013 as shown below), and drag the Fill Handle to project 2014 beginning budget values. (Please note that in this example I have selected the entire columns and the Fill Handle is shown in the upper right hand corner of the selected range.) Using the Fill Handle to Create a Budget for 2014 based on Five Years of Actual Data Why Does This Work? But why does this work? How can a simple drag of a mouse create a sophisticated budget? To better understand the underlying workings of this concept, let’s start with a more simplified example using simple regression. www.CarltonCollins.com Page 29 Copyright April 2013 Using Excel to Detect Fraud Simple Regression Example: In the screen below we start with three columns of data for the months of January, February and March. Start with Three Simple Columns of Data Simply highlight the three columns and drag the Fill Handle out an additional three columns. The result is that Excel fills in new columns for April, May and June – including column headings, column totals and forecast data, as pictured below. The Fill Handle Uses Regression to Project April, May and June Explaining Regression: So where does this new data come from? The answer is that Excel uses linear regression to produce this data. Excel evaluates the data for January, February, and March on a row by row basis, and uses this information to project the subsequent variables. To help you better understand this concept, here is how regression works from a visual perspective: 1. Once again, a simple example using Excel’s Fill handle. The 8 month’s of data yields a projected value of 5,967. www.CarltonCollins.com Page 30 Copyright April 2013 Using Excel to Detect Fraud 2. This time we use the same data, but instead of using the Fill handle, we use the SLOPE and INTERCEPT functions to solve for month 9’s projected value. As you can see above, the slope and intercept functions produce the exact same result as does dragging the Fill Handle, thus proving that the math used by Excel is accurate. Yet another way to produce the same results is to use the FORECAST function, as follows: As you can see in this above example, the FORECAST function also produces the same result as the Fill Handle and the SLOPE & INTERCEPT calculations. All three of these forecast calculations, which produce the same identical values, can be viewed visually by creating a Scatter Chart, and then applying a Trendline, as follows: www.CarltonCollins.com Page 31 Copyright April 2013 Using Excel to Detect Fraud The dotted trend line above is based on linear regression as described in the preceding paragraphs. To forecast future values, Excel simply extends this trend line, and then uses the intervals of the original data to plot future values, as suggested by the red dotted arrows below. Now watch what happens when we base the trendline on logarithmic regression instead of linear regression. In the chart below, we see that the trendline is now curving slightly. Non-Linear Regression: Excel provides 5 forms of non-linear regression (as shown in the Trendline Options box in the image above) – Exponential, Logarithmic, Polynomial, Power and Moving Average. Collectively, these 5 Trendline options are based on different forms of non-linear regression, which is explained in detail on this Wikipedia page http://en.wikipedia.org/wiki/Nonlinear_regression. The Wikipedia’s explanation is very complicated, but to simplify: non-linear calculations weight the data points differently based on their position on the trendline (with linear regression all data points are weighted the same). Some mathematicians and CPAs maintain that non-linear methods produce more accurate results as more recent data points tend to be more relevant to producing a trend than older data points. You can calculate forecast values in Excel using the Exponential form of regression by using the GROWTH function, as follows. www.CarltonCollins.com Page 32 Copyright April 2013 Using Excel to Detect Fraud Notice that the projected value for month 9 is 5,995.86 using Exponential regression, which in this example which is 29.22 higher than the projected value based on linear regression. The simplest way to forecast values using Exponential regression is to drag the Fill Handle while holding down the right mouse button, then selecting Growth from the popup menu as pictured below. This action will fill in the 9th month with a forecast value based on exponential regression instead of linear regression. LINEST and TREND Functions Although not used in this case study, you should be aware that Excel provides two additional forecasting functions - LINEST and TREND. These functions basically forecast values using linear regression exactly like the FORECAST function. The FORECAST and TREND functions are simpler to use than LINEST, but the advantage of the LINEST function is that it can also be used as an Array function to fill in values for a large range of data. Presented below is a simple example of the LINEST function. www.CarltonCollins.com Page 33 Copyright April 2013 Using Excel to Detect Fraud Data Analysis ToolPak To use the LINEST function most efficiently, you should first load Excel’s Analysis ToolPak, as follows. From the File tab, select Options, Add-Ins. In the Manage box, select Excel Add-ins, then click Go. In the Add-Ins dialog box, select the Analysis ToolPak check box, and then click OK. The Data Analysis ToolPak will then appear in your Data Ribbon. The Data Analysis ToolPak’s Regression analysis tool uses the LINEST function to perform more complicated regression analysis which includes controlling the confidence levels and calculating and plotting residuals. The screenshot below shows an example of the Analysis ToolPak’s Regression tool along (shown in the dialog box) and an example of the output generated by this tool beginning in column H. As you can see the output is very complicated, but the resulting output can then be used to fine tune various regression calculations. www.CarltonCollins.com Page 34 Copyright April 2013 Using Excel to Detect Fraud Closer inspection of the ToolPak’s regression tool reveals options for setting the Constant to Zero, adjusting the Confidence Level, and utilizing a combination of Residuals, Standardized Residuals, Residual Plots, Line Fit Plots, and Normal Probability Plots. These detailed aspects of regression are beyond the scope for our particular budgeting purposes, but following are links for those that wish to delve further: 1. The 2002 report Using Dummy Variables in Regression by Hun Myoung Park of Indiana University (www.iuj.ac.jp/faculty/kucc625/documents/dummy.pdf is a good place to start for educating yourself about these variables. 2. This Wikipedia page titled Errors and residuals in statistics goes further in depth into residuals. (http://en.wikipedia.org/wiki/Errors_and_residuals_in_statistics) 3. A 6-page Duke University report walking you through an example for using the Data Analysis ToolPak’s Regression tool is available here (http://tinyurl.com/cueqap2). Shortcomings with the Data Analysis ToolPak’s Regression Tool: To be fair, I should point out that Excel’s ToolPak Regression tool has a number of shortcomings, including: 1. Missing Functionality – Other regression tools offer hierarchical regression and case weighting, but Excel’s tool does not. 2. Inadequate Diagnostic Charts - Several common diagnostic charts are not included in Excel (for example, scatterplot charts, residuals against predicted values, and normality plot of the residuals.) Charting typically goes hand-in-hand with forecasting to help visualize the results. 3. No Standardized Coefficients – Without a standardized coefficient, it may be difficult to interpret your results. www.CarltonCollins.com Page 35 Copyright April 2013 Using Excel to Detect Fraud 4. Inadequate Diagnostic Statistics – The lack of collinearity diagnostics makes it more difficult to understand the forecast data model, although Excel’s PEARSON, RSQAURE and SKEW functions could be used to aide in understanding. Regression Warning Regression only works when the underlying data follows a consistent trend. If revenue has grown steadily for the past six years, then regression will likely project a reasonable value for year seven. However if revenue has jumped all over the board for the past six years, then regression will likely give you a worthless projection for year seven. For example, consider that in the past five years gasoline prices jumped from $1.60 per gallon to more than $4.00 per gallon. If you use regression to predict gasoline prices for future years based on this prior increase, regression will likely predict gasoline prices in the $10.00+ per gallon range – but let’s hope that such a prediction would be inaccurate – right? Testing Data’s Suitability for Regression Calculations Therefore, you should always visit each line item in the projection and consider whether the projected values make sense. Excel provides at least two good functions to help you accomplish this task – PEARSON and RSQUARE. For example, in the screen shot below, I have calculated the suitability of 5 different sets of data for regression, using both the PEARSON and R SQUARE functions. The first data set on row three has a perfect trend and scores a 100% in both the PEARSON and R SQUARE calculations. However, the data sets that follow are comprised of an increasingly less perfect trend, and the declining PEARSON and R SQUARE scores reflect this decline. www.CarltonCollins.com Page 36 Copyright April 2013 Using Excel to Detect Fraud For example, I might conclude that the first four sets of data were found to have a sufficient trend as to provide a suitable basis for regression calculations but that the data set in row 15 does not. You should establish your threshold and consistently stick to that threshold. In this case, I might require a minimum 80% PEARSON score and 65% R Square score in order to justify reliance on that data as a basis for regression forecasting. Two More Statistical Measures Two other Excel functions that might also be useful for analyzing the suitability of data for regression include KURTOSIS and SKEW, which both measure the symmetry of data along a bell curve. For example, data that is perfectly symmetrical will yield a SKEW score of 0 (zero). The closer a data’s SKEW is to zero, the less suitable that data is for regression, because the data’s trend is considered unreliable, be it trending upwards or downwards. The KURTOSIS works similarly, although it’s scoring is different as it is designed to measure multiple peaks, whereas the SKEW measures a single Peak. Alternatives To Regression If data is found to be inadequate for regression calculations, then other forecasting methods will be necessary. For example, you might: 1. Inflation Forecasting - Forecast future amounts based on prior year amounts inflated for inflation, increases in the consumer price index, or some other inflation factor. 2. Percentage Forecasting - Forecast future amounts as a percentage of another line item, such as sales or payroll. For example, Cost of Goods Sold (COGS) might be forecast as 45% of forecast Sales since historically, COGS does approximate that percentage amount. Or you might forecast Fringe Benefits as 15% of Payroll since historically, Fringe Benefits do approximate that percentage amount. 3. Best Guess Forecasting - You might come up with another forecast amount based on discussions with department heads. For example, the training budget might be forecast much higher than regression, inflation, or percentage methods because you know that since the new version of Windows 8 and Office 2013 will be implemented, a significantly higher than normal amount of training will be needed to bring everyone up to speed on those products. Always Use Your Better Numbers When You Have Them (This should be obvious to all, but I will say it anyway…) Of course some budget line items should never be forecast using regression or other forecasting methods because they are known amounts. For example, regression may suggest that rent expense might be $236,433.12 for January 2014, but since I have signed a lease agreement, I know that rent expense will be exactly $220,000 for January 2014, so that is the amount I will use. The same goes for known line items such as depreciation expense, web-hosting expenses, interest payments on outstanding loans, www.CarltonCollins.com Page 37 Copyright April 2013 Using Excel to Detect Fraud and any other contractually known obligations. You would always use these more accurate numbers instead of regression’s projected numbers. Critical Key Point to Understand The key point is that regression represents a starting point for many of the budget line items, but not all budget line items. In all probability, a combination of forecasting methods will need to be applied depending on each particular line item – regression should not be relied upon for all forecast data. Detailed Budget Example Using Regression Starting with Dynamics GP Now that we’ve discussed the various concepts related to regression, you are now ready to see it in action. In this example, we will start by exporting 4 years’ worth of income statement data from Dynamics GP to Microsoft Excel (virtually every accounting system on the planet enables users to complete this step). In Dynamics GP, we start by printing a 36-month income statement to the screen (as pictured below) and exporting it to Excel. www.CarltonCollins.com Page 38 Copyright April 2013 Using Excel to Detect Fraud Next in Excel, Regression Creates the Initial Budget Once in Excel, to create the initial budget, select the 36 columns with numeric data, then left click and drag the “Fill Handle” out twelve additional columns to create the 2014 budget, as suggested below. The result is that Excel uses linear regression analysis to predict the future values. Keep in mind that this is just an initial starting point. Overwrite those line items where you have better numbers Once we have completed this process, we then insert better numbers on those line items where we have better budget amounts. For example, the current lease agreement will provide the most accurate amount to use for rent expense. We would use our depreciation schedule to provide the most accurate amounts for depreciation expense. For interest expense, we would look to the loan amortization schedule to prove these numbers (and so on). However for those numbers where you have no better basis to use for budget preparation purposes, why not use linear regression analysis to provide the answer? To accomplish this task, it is best to use the split screen tool to split the screen into four areas so you can easily see the row descriptions and column headings for the corresponding budget line items you are working with. (Excel 2013 no longer provides split screen tools on the scroll bars as did Excel 2003, 2007 and 2010 – you must click the Split Screen tool icon on the View tab and then adjust the splits by dragging them). Now scroll each line item and ask yourself if you have a more accurate basis for forecasting that line item, and if so, insert those more accurate values. For example, I have inserted new depreciation values (highlighted in grey) in the screenshot below. www.CarltonCollins.com Page 39 Copyright April 2013 Using Excel to Detect Fraud Document Your Budget Values For each line item you change, you should document the basis for that budget line item with an Excel comment, (or some other method such as an adjacent in-cell comment). For example, in the screenshot below, I have inserted Comments next to each account description indicating the line item’s forecasting basis. Comments are indicated by small red triangles in the upper right corner of a cell and the comment is displayed whenever you hover over the red tick mark with your mouse. To print comments, select Page Setup from the Page Layout tab, and on the Sheet tab select At end of the sheet from the Comments dropdown box, as pictured on the left below. Note that the comments do not show up in Print Preview, but they do appear as a printed page at the end of your print out; an example of which is pictured on the right below. www.CarltonCollins.com Page 40 Copyright April 2013 Using Excel to Detect Fraud Testing Data for Regression Suitability Next we will test each line item’s data for regression suitability. This step will help us determine which rows, if any, need to be forecast using a method other than regression. We start this process by labeling a couple of blank columns Pearson and R Square, then enter the respective formulas to test the 36 columns of data row–by–row, as shown below, on the left. Notice that both the PEARSON and R SQUARE formulas return percentage values that are both negative and positive, which means the data is trending upward or downward. Since we don’t care which direction the data is trending, (we only care that it scores high), we can edit the formulas to include the ABSOLUTE function (ABS) which changes all amounts to positive numbers, as picture above on the right. Now we can set our thresholds to minimum scores, let’s say 50% (Pearson) and 40% (R Square) for example, then apply conditional formatting to flush out those line items that meet our stated criteria. As pictured to the right, those line items in columns BB and BC containing formatting are not suitable for regression based on our stated criterion level, and another forecasting method will need to be used to forecast those amounts. For example, we may simply use last year’s number inflated by the consumer price index. www.CarltonCollins.com Page 41 Copyright April 2013 Using Excel to Detect Fraud Budget Totals Now that we have generated regression amounts, and overwritten those amounts where we have more accurate numbers and also those where regression is not suitable, we continue by totaling the 12 months to produce the annual 2014 budget amounts, as pictured below. The purpose of totaling the annual budget is so we can adjust the monthly budget for seasonality, as discussed below. Adjusting for Seasonality Annual budget amounts are not very useful because they do not allow you to compare actual to budgeted results on a monthly basis – you must produce monthly budget amounts. However, simply dividing an annual budget by 12 to produce monthly amounts is not good enough because many line items are typically seasonal. For example, actual revenue may be twice as high in some months compared to other months, but comparing these seasonal sales amounts to a nonseasonal budget is virtually meaningless because you can’t tell whether you are on target, off target, or by how much. Therefore, it is difficult to determine whether corrective measures are needed on a month to month basis. Seasonal budgets make a big difference. I believe one of the primary reasons companies fail to properly analyze their budgets to actuals throughout the year is because their budgets are not seasonal to begin with, and therefore such comparisons are virtually meaningless. To add seasonality to your budget; simply spread the annual amount of each budget line item across the 12 months based on the ratio of last’s year’s monthly amounts compared to last year’s annual amount, as follows. www.CarltonCollins.com Page 42 Copyright April 2013 Using Excel to Detect Fraud Start by creating column headings for the seasonal budget, as pictured below. Next, enter a formula using last year’s January value (as of January 2013) as a the numerator and the SUM of all of 2013’s values as the denominator, and then multiplied times the 2014 annual budget amount (=AD6/SUM($AD6:$AO6)*$BB6), as pictured below. Notice in this formula I have used dollar signs to anchor the column references so that I may copy the formula down and across to complete the seasonality adjustments. Rounding & Formatting It is rather senseless to produce budgets with pennies, or even dollars; I recommend rounding the results by editing the seasonality formula. Edit the seasonality formula adding the ROUND function in front of the formula and “-2” to the end of the formula to round to the nearest hundredths, as pictured. Now recopy this revised formula (overwriting the previous seasonally adjusted budget data) to update the budget. www.CarltonCollins.com Page 43 Copyright April 2013 Using Excel to Detect Fraud Finally, select a formatted column (such as column BA in this example) and click the Format Painter tool; then highlight the twelve months budget to apply the formatting, as suggested below. This Income Statement Budget Is Not Yet Completed At this point, we have prepared a complete monthly budget using regression supplemented with other forecasting methods, and this effort may be sufficient for your needs. However, please be aware that this budget example was simplified in order to more easily convey Excel’s regression tools and concepts. There is more to the process for those truly dedicated to creating the most accurate budget possible – keep reading. Forecasting Revenue In the example above, for the purpose of explaining regression as simply as possible, I treated the budgeting process for revenue exactly the same as the budgeting process for expenses, but in reality budgeting revenue is usually a different process from budgeting expenses. For established companies, many projected expenses can be reasonably determined using regression, inflation, percentage of sales or best guess forecasting methods. However, revenue is subject to far greater external factors such as competition, marketing, the state of the economy, inflationary pressures, changing attitudes, etc. For example, the appearance of a new competitor in the marketplace could steal away market share and thus negatively impact www.CarltonCollins.com Page 44 Copyright April 2013 Using Excel to Detect Fraud revenue. For example, in late 2012 Apple shares fell from $700 a share to almost $400 a share for no other reason than the prospects that Microsoft’s, Google’s and Samsung’s new tablet PC offerings were expected to eat into Apple’s market share. Negative press related to the quality of your product (such as the gas pedal sticking for Toyotas) could adversely affect sales. By contrast, your product may become wildly popular if a well know celebrity starts wearing or using your product. A good marketing campaign can help significantly, or hurt if it happens to make the wrong impression. The point is that regression is unable to incorporate factors like this, therefore a more detailed forecasting approach is usually needed. A good budget will consider all of the relevant factors and in the end, you may produce multiple budgets given differing anticipated scenarios. Simple Example of Revenue Projection Based on Units In the following example, Crazy Fred’s has listed the number of training courses scheduled for each month of the budget year, and has projected attendance for each month based on the average attendance achieved in previous years for those same months. Crazy Fred charges a course fee of $100 per attendee, which is input in cell A8. Crazy Fred also knows that the fixed cost of printing the training manual and having the food catered will be $22 and $27, respectively – as input into cells A11 and A12. Notice that this projection method does is not based on historical revenue amounts, only historical attendance figures have been used. In this example, the company knows how many classroom venues have been booked and has a fairly decent idea as to what attendance might be; therefore, regression based on historical revenue amounts would not be as accurate as using these known quantities to forecast revenues. A more sophisticated example of forecasting revenues based on units of production is shown below. In this example, a CPA firm has listed each employee along with each employee’s budgeted billable hours and billing rates by month. www.CarltonCollins.com Page 45 Copyright April 2013 Using Excel to Detect Fraud In this example, projected revenue is again based upon units rather than historical revenue amounts, as regression methods applied to historical revenue amounts would likely yield less accurate projections. Keep in mind that revenue is often more volatile than expenses. An effective marketing program might increase the number of units sold, a bad economy might adversely affect the number of units sold. Any foreseen or expected events like these should be incorporated into the budget and explained in detail. In conclusion, while the regression example above was used to forecast both revenue and expenses, in many cases regression should probably only be used as a means of forecasting expenses only. www.CarltonCollins.com Page 46 Copyright April 2013 Using Excel to Detect Fraud Budgeting Balance Sheets and Cash Flow In many cases, budgets consist of a profit and loss statement only, but I believe this falls short. By creating a budgeted balance sheet and cash flow statement, (which requires the creation of a budgeted balance sheet), a company can truly monitor expected results for every account, including the all-important cash flow balance. The process starts by forecasting the balance sheet and once created, forecasting cash flow is a simple matter of crunching the numbers. To produce a budgeted balance sheet, assumptions are needed related to the days in accounts receivable, accounts payable and inventory. These day calculations are best derived by examining the historical days in accounts receivable, accounts payable and inventory for recent years, and using those amounts as a guide. For example: 1. AR - The budgeted accounts receivable balance may be calculated as 46 days of the prior month’s sales. 2. AP - The budgeted accounts payable balance may be calculated as 28 days of the prior month’s variable expenses. 3. Inventory - The budgeted inventory balance may be calculated as 62 days of the prior month’s COGS amount. 4. Loan Payments – Loan repayments should be budgeted based on the actual amortization schedules, based on the principle payment amounts. 5. And so on. Once the balance sheet items have been budgeted, the resulting cash flow budget is computed as follows: www.CarltonCollins.com Page 47 Copyright April 2013 Using Excel to Detect Fraud The area in yellow (rows 5 through 19) shows the profit and loss budget as projected using the methods described earlier above. The blue area (rows 21 through 25) depicts the assumptions and the changes in balance sheet balances. The green areas (rows 26 through 32) represent the forecast balance sheets and cash flow forecast. Because the income statement is seasonalized, the balance sheet balances and cash flow forecast will also be seasonalized. www.CarltonCollins.com Page 48 Copyright April 2013 Using Excel to Detect Fraud Profit Margin Monitoring & Calculating Your Desired Profit Margin It is also useful for companies to budget and monitor their profit margins; a profit margin that misses its target speaks volumes. Once established, budget to actual profit margin comparisons can also be used as benchmarks to help detect fraud, errors or irregularities. To calculate your desired profit margin, I suggest that you work backwards by asking yourself (or your client) two simple questions, for example: Let’s assume that Burt has owned and operated a construction company store for the past 17 years. As his CPA, I ask him two questions as follows: How much profit do you want to make next year and how much sales do you anticipate next year? Burt responds – “that’s easy, we’ve been growing at 8% a year for the past five years and last year (2013) we nearly reached $12 million sales, so we will probably hit $13 million in revenue next year (2014). Also, I’d like to make a million dollars profit – I think that’s a reasonable goal.” With just this little bit of data, we can work backwards based on Burt’s prior year financial statements and advise him as follows: www.CarltonCollins.com Page 49 Copyright April 2013 Using Excel to Detect Fraud Burt’s fixed costs are a little more than $6 million in 2012, but let’s say that we can adjust this amount down to $5,200,000 because the company was able to renegotiate and sign a new lease agreement. The point is that we are using 2012’s fixed cost amount along with any known adjustments. This allows us to work backwards to calculate the projected Gross Margin of $6,200,000. www.CarltonCollins.com Page 50 Copyright April 2013 Using Excel to Detect Fraud From here we can compute Cost of Goods Sold, and then divide Cost of Goods Sold and Gross Margin by Sales to derive the desired Profit Margin that will cover Fixed Costs, Variable Costs and still have the desired Net Income of $1,000,000 left over. In conclusion, a Profit Margin of 47.7%will yield the desired results. Now that the optimum profit margin is known, let’s say that further analysis reveals that the inventory and labor items on average are priced at just 44.5% above cost, as the following calculations show, net income for 2014 would only be expected to reach $585,000 – well below Burt’s desired profit. www.CarltonCollins.com Page 51 Copyright April 2013 Using Excel to Detect Fraud At this point, you need to convince Burt of the importance of pricing his products and services at the desired profit margin in an effort to target the desired results. To convey this point, you will tell Burt the following laughable story about the Florida boys who started a business in Gainesville, Florida selling onions. It goes like this: These two Florida boys were running up to Georgia and buying Vidalia onions at 4 for $1.00 which they then took back to Gainesville and sold for a quarter a piece on the www.CarltonCollins.com Page 52 Copyright April 2013 Using Excel to Detect Fraud streets. The business was an instant success and soon those boys found themselves selling from a road side stand, to a small store, to a much bigger store. The customers kept coming and the business kept getting bigger. Soon they had customers lined up around the block to buy those onions, which they kept buying 4 for a dollar and selling for 25 cents apiece. After six months, one Florida boy turned to the other and said – “you know, business is great! But I don’t think we’re making any money – what do you think we should do?” The other Florida boy thought real hard and then blurted – “I think we need a bigger truck.” OK, it’s an old exaggerated story, but there is a lesson to be learned here. If you don’t price your products to make a profit, you will never make a profit. And, if you don’t price your products to make your desired profit, you will never make your desired profits. In our example above, Burt should consider setting his margin pricing to target a profit margin of 47.7%, instead of the current profit margin of 44.5% to ensure a chance of achieving his desired goals. Without this measure, Burt has absolutely no chance of reaching his goals, unless his revenue estimate is wildly under-stated. To be sure, if Burt’s costs go up or down, his prices will need to be adjusted accordingly to provide the desired profit margin. But when you think about it, this approach is one in which Burt sells his goods and services to his customers at the lowest price point possible that covers his fixed costs, variable costs, and desired profit – and not a penny more. It seems reasonable that every company in the world strive for this goal - right? Here’s a simplified way to look at this - suppose your business was to purchase candy bars for resell. Your only options are to sell the candy bars for: A. B. C. D. E. Below cost. At cost. At cost plus your desired profit. At cost plus an egregiously high profit. At cost plus some random profit that may or may not be sufficient. I can’t see how any reasonable person could select any option other then C – yet I see many companies sell their products based on all of these scenarios because they don’t take time to calculate their desired profit margin, and then monitor that amount throughout the year. You can download this Profit margin template at www.CarltonCollins.com/profitmargin.xlxs. www.CarltonCollins.com Page 53 Copyright April 2013 Using Excel to Detect Fraud Proof of Cash (Four-Column Bank Reconciliation) Many auditors use a four-column bank reconciliation, also known as a Proof of Cash, to help shed light on error, misstatements, and fraud. The proof of cash reconciles the bank balance and general ledger over a specified period of time, whereas, the standard bank reconciliation reconciles the two at a specific date. Download the monthly proof of cash template here: www.CarltonCollins.com/5proof.xls. www.CarltonCollins.com Page 54 Copyright April 2013 Using Excel to Detect Fraud Purpose of Proof of Cash The purpose of this report is to test cash transactions for a given period to verify the existence and completeness assertions, as it relates to transactions. This report is frequently used when internal controls over cash transactions are not effective and an audit of the ending cash balance is not enough because of corollary misstatements to other accounts that result from unrecorded cash transactions or fictitious transactions. This report specifically tests for the following four items: 1. Tests that all recorded (on the client's books) cash receipts were actually deposited. (existence) 2. Tests that all receipts deposited in the bank were recorded on the client's books. (completeness) 3. Tests that all recorded cash disbursements were processed by the bank. (existence) 4. Tests that all disbursements processed by the bank were recorded. (completeness) The standard 4 column proof of cash reconciles client books and records with 3 rd party bank records for beginning (column 1) and ending (column 2) balances, as well as cash receipts/deposits (column 2) and cash disbursements/charges (column 3) for a given period. Usually one starts with amounts from the bank statement(s) and reconciles to what is reflected in the client's general ledger and/or cash receipts and disbursements journal, as explained below: Reconciling Item Beginning Deposits in Transit Column Affected 1 Beginning Deposits in Transit 2 Ending Deposits in Transit 2 Ending Deposits in Transit 4 Beginning Outstanding Checks 1 www.CarltonCollins.com Explanation You must add this amount to the beginning cash balance per bank because the bank did not receive the deposits before the prior month cut off. You must subtract this amount from the deposits shown by the bank for the period because these were recorded on the client's books in prior period. You must add this amount to the deposits shown by the bank for the period because these were recorded on the client's books this period, but will not be received by the bank until next period. You must add this amount to the ending balance per the bank because the bank did not receive them until after the end of the period, but they were recorded on the client's books this period. You must subtract this amount from the beginning cash balance per bank because the bank did not Page 55 Copyright April 2013 Using Excel to Detect Fraud Beginning Outstanding Checks 3 Ending Outstanding Checks 3 Ending Outstanding Checks 4 Customer NSF Checks redeposited by client in same period Customer NSF Checks redeposited by client in same period 2 Customer NSF Checks redeposited by client in the following period 3 Customer NSF Checks redeposited by client in the following period 4 3 receive the checks for processing before the prior month cut off. You must subtract this amount from the disbursements/charges shown by the bank for the period because they were recorded on the client's books in the prior period. You must add this amount to the disbursements/charges shown by the bank for the period because they were recorded on the client's books in this period, but will not be received by the bank for processing until next period. You must subtract this amount from the ending cash balance per bank because the bank did not receive the checks for processing until after this month's cut off, but have been recorded on the client's books. You must subtract this amount from deposits per bank because the client did not record the second deposit as an additional receipt. You must subtract this amount from disbursements/charges per bank because the return of the NSF check was not recorded on the client's books as a cash disbursement. You must subtract this amount from disbursements/charges per bank because the return of the NSF check was not recorded on the client's books as a cash disbursement. You must add this amount to the ending balance per the bank because the bank reduced the balance when the check was returned NSF by the customer's bank and the client did not record it as an additional disbursement and it is basically a DIT at period's end. To Prepare a Proof of Cash: 1. Start with a beginning balance, typically a year-end balanced previously reconciled. 2. Reconcile receipts 3. Reconcile disbursements. 4. Complete it with the ending balance, typically the current year-end. www.CarltonCollins.com Page 56 Copyright April 2013 Using Excel to Detect Fraud The actual completion of this reconciliation can be relatively complex and time consuming. Comments About the Proof of Cash Template: 1. Hints are included in comments for each cell (see red triangles in the template) 2. Check figures - each column must foot and cross foot (meaning the columns must balance as well as the rows). Items that don't add up correctly are automatically highlighted. 3. The dates entered at the top of the reconciliation automatically populate the rest of the reconciliation. 4. An example reconciliation has been included on the second worksheet. Tick marks are included to help document your work. www.CarltonCollins.com Page 57 Copyright April 2013 Using Excel to Detect Fraud Excel Data Cleaning CPAs often receive or retrieve data from many sources in a wide variety of formats such as Text, Comma-Separated-Value (CSV), or Web Page formats. You often cannot have control over the format and type of data that you import. However, before you can analyze the data in Excel, you may need to clean it up first. Fortunately, Office Excel has many features to help you clean data and prepare it for analysis. By data cleaning, you may think I mean reformatting the data so that the information is more readable and font and font sizes are appropriate, but this is not the kind of data cleaning I am talking about. What I am referring to is the need to separate data in one cell to multiple cells, to repeat row descriptions where needed; add column headings, spell check for spelling errors, remove duplicate rows, remove trailing spaces, eliminate leading zeros, etc. Some of Excel’s top functions and commands for cleaning data are as follows: 1. Spell checker 2. Import 3. Text to Columns 4. Remove Duplicates 5. Find & Replace 6. Spell Check 7. =UPPER 8. =LOWER 9. =PROPER 10. =FIND 11. =SEARCH 12. =LEN 13. =SUBSTITUTE 14. =REPLACE 15. =LEFT 16. =MID 17. =RIGHT 18. =VALUE 19. =CONCATENATE 20. =TEXT 21. =TRIM 22. =CLEAN 23. =FIXED 24. =DOLLAR 25. =CODE 26. Macro www.CarltonCollins.com Page 58 Copyright April 2013 Data Cleaning Strategies 1. Importing Data into Excel – Of course Excel opens Excel files, but what happens when you attempt to open other file formats? The answer is that Excel attempts to automatically import that data on the fly and displays an Import Wizard to help you complete the process. The Text Import Wizard examines the text file that you are importing and helps you import the data the way that you want. To start the Text Import Wizard, on the Data tab, in the Get External Data group, click From Text. Then, in the Import Text File dialog box, double-click the text file that you want to import. The following dialog box will be displayed: If items in the text file are separated by tabs, colons, semicolons, spaces, or other characters, select Delimited. If all of the items in each column are the same length, select Fixed width. In step 3, click the Advanced button to specify that one or more numeric values may contain a trailing minus sign. Also click the desired data format for each column to be imported. Using Excel to Detect Fraud 2. Text to Columns – The Text to Columns command located on the Data Ribbon works exactly the same way as described above – the user simply launches it to convert data within an existing worksheet. 3. No Built-in Logic Checking - Excel’s Text to Columns tool does not automatically recognize delimiters (commas, spaces, or quotes), although it may sometimes appear so. Like an elephant, Excel’s Text to Columns simply has a good memory. Each time you use Text to Columns, it remembers your parsing criteria and sets it as the default setting for future parse jobs, until Excel is closed. Some CPAs use the Text to Columns tool, changing the delimiter criteria as necessary, then they open a second file containing the same type of delimited data. In this second case, it may appear to them that Excel automatically recognized the embedded delimiter, but it was only following the lead from the first parsing job. www.CarltonCollins.com Page 60 Copyright April 2013 Using Excel to Detect Fraud Note: There is no option for changing the default Text to Columns delimiter in Excel, but you can achieve the same effect by setting the desired delimiters in a workbook and saving it as a template or as the default workbook. You could also create macros designed to parse data according to the delimiting criteria you frequently encounter. 4. Removing Duplicate Rows - Duplicate rows are a common problem when you import data. You can identify and remove duplicate rows by using the Data, Advanced Filter, Unique Records Only tool as show in the screen below. 5. Find and Replace Text – This tool can be used to identify and remove leading strings, such as a label followed by a colon and space, or a suffix, such as a parenthetic phrase at the end of the string that is obsolete or unnecessary. You can do this by finding instances of that text and then replacing it with no text or other text. Noteworthy Find and Replace Points: 1. 2. 3. 4. You can find and replace for an entire worksheet, or the entire workbook. You can find and replace formats with new formats. There is a cell chooser option that makes it easier to find and replace formats. If you highlight a range of cells, then Find and Replace only finds and replaces within that range of cells. 5. You can replace all at once or one at a time. 6. You could also find and replace references in a formula. www.CarltonCollins.com Page 61 Copyright April 2013 Using Excel to Detect Fraud 7. Searching for a “[“ is the only way to locate external links. 6. Find and Replace in Word, rather than Excel – In some cases, it may make more sense to first paste your data into Word, and then use the Find and Replace tool in that environment, rather than Excel. This is particularly useful when you are working with row data you want organized into paragraphs. For example, the easiest way to remove unwanted line breaks is to simply press Alt + Ctrl + K. This keystroke combination runs Word’s AutoFormat command which analyzes the document and instantly applies an appropriate format, which includes the removal of unnecessary line breaks. (In this case, you should consider adding the AutoFormat tool to your Quick Access Toolbar for easy access.) However, depending on the document, AutoFormat may delete (or alter) formatting you wanted to keep. In this case, you are on the right track using the search and replace method as you described, you just need to add two small steps to your procedure to obtain the desired results. If you look closely, you will see that your document contains single paragraph breaks where you don’t want them, and double paragraph breaks where you do. The trick is to get rid of the breaks you don’t want but keep the breaks you do. Start by replacing the double paragraph breaks (which you do want to keep) with an uncommon sting of text that does not appear in the document, such as “5555”, as follows. From the Office 2010 or 2007 Home tab, select Editing, Replace to launch the Find and Replace dialog box. From the Office 2003 menu select Edit, Replace to launch the Find and Replace dialog box. Then, in the Find what box, type ^p^p, and in the Replace with box type 5555, as shown. Click the Replace All button. Next, use find and replace again to remove all single paragraph breaks (which you don’t want) as follows: From the Home tab, select Editing, Replace and type ^p in the Find what box and enter a space in the Replace with box, then click the Replace All button (this action will remove all paragraph breaks from your document and adds spaces instead). Finally, restore the double paragraph breaks as follows: From the Home tab, select Editing, Replace and type 5555 in the Find what box and ^p^p in the Replace with box, then click the Replace All button. Your resulting www.CarltonCollins.com Page 62 Copyright April 2013 Using Excel to Detect Fraud document will be devoid of the unwanted paragraph breaks and you will be able to copy, paste and edit the text unencumbered by unwanted line breaks. 7. Spell Check - You can use a spell checker in Excel to not only find misspelled words, but to find values that are not used consistently, such as product or company names, by adding those values to a custom dictionary. The spell check function also checks your grammar as well. 8. Changing The Case Of Text – You can use one or more of the three Case functions to convert text to lowercase letters, such as e-mail addresses; uppercase letters, such as product codes; or proper case, such as names or book titles. Using a specific case in Excel is not necessary because Excel ignores case for all function calculations (such as VLOOKUPS); however, you may want to convert case for consistency and for better visual appeal and readability. Excel does utilize case when performing a Search when the Match Case checkbox is selected, or when performing a Sort when the Case Sensitive checkbox is selected. a. = UPPER - Converts text to uppercase letters. b. =LOWER - Converts all uppercase letters in a text string to lowercase letters. www.CarltonCollins.com Page 63 Copyright April 2013 Using Excel to Detect Fraud c. =PROPER - Capitalizes the first letter in a text string and any other letters in text that follow any character other than a letter. Converts all other letters to lowercase letters. 9. Merging And Splitting Columns - A common task after importing data from an external data source is to either merge two or more columns into one, or split one column into two or more columns. For example, you may want to split a column that contains a full name into a first and last name. Or, you may want to split a column that contains an address field into separate street, city, region, and postal code columns. The reverse may also be true. Presented below are functions that to help you accomplish these tasks: 10. =FIND – Returns the starting position of a character, string of characters or word with a cell. Find is case sensitive. 11. =SEARCH – Returns the starting position of a character, string of characters or word with a cell. Search is not case sensitive. 12. =LEN – Displays the length or number of characters in a cell. 13. =SUBSTITUTE – Replaces a character or characters with a character or characters that you specify. 14. =REPLACE - Replaces a character positions with a character or characters that you specify. www.CarltonCollins.com Page 64 Copyright April 2013 Using Excel to Detect Fraud 15. =LEFT – Extracts the specified number of characters from a cell, starting from the left. 16. =MID – Extracts the specified number of characters from a cell, starting from somewhere in the middle of the cell. 17. =RIGHT – Extracts the specified number of characters from a cell, starting from the right. 18. =Value – Converts text to values so the data can be added, subtracted, multiplied, divided or referenced in a function. 19. =CONCATENATE - Joins two or more text strings into one text string. Just to make you aware, Excel provides the following variations of these functions for use when working with foreign languages or foreign characters like these ( "," ) =FINDB; =SEARCHB; =REPLACEB; =LEFTB; =RIGHTB; =LENB; and =MIDB. www.CarltonCollins.com Page 65 Copyright April 2013 Using Excel to Detect Fraud Cleaning Text – (Removing Spaces And Nonprinting Characters From Text) - Sometimes text values contain leading, trailing, or multiple embedded space characters ( character set values 32 and 160), or nonprinting characters (Unicode character set values 0 to 31, 127, 129, 141, 143, 144, and 157). These characters can sometimes cause unexpected results when you sort, filter, or search. For example, in the external data source, users may make typographical errors by inadvertently adding extra space characters, or imported text data from external sources may contain nonprinting characters that are embedded in the text. Because these characters are not easily noticed, the unexpected results may be difficult to understand. Following is a list of functions you can use to remove these unwanted characters: 20. =TEXT - Converts a value to text in a specific number format. 21. =TRIM - Removes the 7-bit ASCII space character (value 32) from text. 22. =CLEAN - Removes the first 32 nonprinting characters in the 7-bit ASCII code (values 0 through 31) from text. 23. =FIXED - Rounds a number to the specified number of decimals, formats the number in decimal format by using a period and commas, and returns the result. 24. =DOLLAR - Converts a number to text format and applies a currency symbol. www.CarltonCollins.com Page 66 Copyright April 2013 Using Excel to Detect Fraud 25. =CODE - Returns a numeric code for the first character in a text string. Fixing Dates and Times - There are many different date formats, and these varied formats may be confused with numbered part codes or other strings that contain slash marks or hyphens. Dates and times often need to be converted and reformatted. Presented below is a list of functions that help you accomplish this task. 26. =DATE - Returns the sequential serial number that represents a particular date. If the cell format was set to General before the function was entered, the result is formatted as a date. 27. =DATEVALUE - Converts a date represented by text to a serial number. 28. =TIME - Returns the decimal number for a particular time. If the cell format was set to General before the function was entered, the result is formatted as a date. 29. =TIMEVALUE - Returns the decimal number of the time represented by a text string. The decimal number is a value ranging from 0 (zero) to 0.99999999, representing the times from 0:00:00 (12:00:00 AM) to 23:59:59 (11:59:59 P.M.). Transforming And Rearranging Columns And Rows - Most of the analysis and formatting features in Office Excel assume that the data exists in a single, flat two-dimensional table. Sometimes you may want to make the rows become columns, and the columns become rows. At other times, data is not even structured in a tabular format, and you need a way to transform the data from a nontabular to a tabular format. The following function can help you achieve this goal: www.CarltonCollins.com Page 67 Copyright April 2013 Using Excel to Detect Fraud 30. =TRANSPOSE - Returns a vertical range of cells as a horizontal range, or vice versa. 31. Data Fill In Trick – A clever trick for filling in missing data can be accomplished using the GOTO, Special, Blanks command. Here is how it works. This trick works well when you have a large volume of data but descriptions are not provided for every row, as shown in the example below: Start by entering a simple formula referencing the data label in the above cell, just like this: www.CarltonCollins.com Page 68 Copyright April 2013 Using Excel to Detect Fraud a. b. c. d. e. f. g. Next copy that formula... Highlight the entire range containing data labels in columns A and B. columns... Press the F5 key to launch the GoTo dialog box... Select the Options Box... Click on the “Blanks” radio button... Press Enter... Paste. This action will cause all data labels to repeat in the empty cells beneath. Next: h. Copy columns A & B... i. Paste Special as values to convert the formulas to text based data labels... j. You are now ready to sort, filter, subtotal and pivot your data. Fetching Data - Occasionally, database administrators use Office Excel to find and correct matching errors when two or more tables are joined. This might involve reconciling two tables from different worksheets, for example, to see all records in both tables or to compare tables and find rows that don't match. www.CarltonCollins.com Page 69 Copyright April 2013 Using Excel to Detect Fraud 32. =VLOOKUP - Searches for a value in the first column of a table array and returns a value in the same row from another column in the table array. For example, consider the example below which uses a =VLOOKUP function to calculate the appropriate amount of tax due based on the IRS rate schedule. As the Income statement shown in the shaded area is updated, the resulting taxable income amount is referenced in Cell F13. Next, 3 VLOOKUP functions pull the appropriate rate, base and threshold information from the rate schedule to be used in calculating income tax. Once calculated, the resulting tax is referenced back to the income statement for the purposes of computing Net income after taxes. Key points to Consider when Using VLOOKUP: a. If you are looking up based on text, the first column containing lookup values must be sorted alphabetically in descending order – else it will not work properly. b. Another approach is to add the FALSE attribute at the end of the VLOOKUP function, to force Excel to lookup values based on exact matches. c. If you are looking up based on text, you must have an exact match between the lookup value and the table array value. d. If you are looking up based on values, the first column containing lookup values must be sorted numerically in descending order – else it will not work properly. e. If you are looking up based on values, then Excel will choose the closest value without going over. For example, if the lookup value is 198,000 and the table array contains values of 100,000 and 200,000, then Excel will choose 100,000 because 200,000 goes over or exceeds 198,000. (It might be helpful to think back to the old Bob Barker game show the Price is Right.) www.CarltonCollins.com Page 70 Copyright April 2013 Using Excel to Detect Fraud 33. =HLOOKUP - Searches for a value in the top row of a table or an array of values, and then returns a value in the same column from a row you specify in the table or array. 34. =INDEX - Returns a value or the reference to a value from within a table or range. There are two forms of the INDEX function: the array form and the reference form. 35. =MATCH - Returns the relative position of an item in an array that matches a specified value in a specified order. Use MATCH instead of one of the LOOKUP functions when you need the position of an item in a range instead of the item itself. 36. =OFFSET - Returns a reference to a range that is a specified number of rows and columns from a cell or range of cells. The reference that is returned can be a single cell or a range of cells. You can specify the number of rows and the number of columns to be returned. 37. Data Cleaning with Macros - To periodically clean the same data source, consider recording a macro or writing code to automate the entire process. There are also a number of external add-ins written by third-party vendors, listed in the Third-party providers section, that you can consider using if you don't have the time or resources to automate the process on your own. 38. RAND( ), RANDBETWEEN( ), ROUND( ) – In Excel 2003, RANDBETWEEN is not in the standard EXCEL 2003 installation but if the analysis tool pack is installed and the add-in activated it is an extremely useful function. www.CarltonCollins.com Page 71 Copyright April 2013 Using Excel to Detect Fraud 39. Informational Functions CELL(info_type,reference) - Info_type is a text value that specifies what type of cell information you want. The following list shows the possible values of info_type and the corresponding results. Info_type Returns "address" Reference of the first cell in reference, as text. "col" Column number of the cell in reference. "color" 1 if the cell is formatted in color for negative values; otherwise returns 0 (zero). "contents" Value of the upper-left cell in reference; not a formula. "filename" Filename (including full path) of the file that contains reference, as text. Returns empty text ("") if the worksheet that contains reference has not yet been saved. "format" Text value corresponding to the number format of the cell. The text values for the various formats are shown in the following table. Returns "-" at the end of the text value if the cell is formatted in color for negative values. Returns "()" at the end of the text value if the cell is formatted with parentheses for positive or all values. "parentheses" 1 if the cell is formatted with parentheses for positive or all values; otherwise returns 0. "prefix" www.CarltonCollins.com Text value corresponding to the "label prefix" of the cell. Returns single quotation mark (') if the cell contains left-aligned text, double quotation mark (") if the cell contains right-aligned text, caret (^) if the cell contains Page 72 Copyright April 2013 Using Excel to Detect Fraud centered text, backslash (\) if the cell contains fill-aligned text, and empty text ("") if the cell contains anything else. "protect" 0 if the cell is not locked, and 1 if the cell is locked. "row" Row number of the cell in reference. "type" Text value corresponding to the type of data in the cell. Returns "b" for blank if the cell is empty, "l" for label if the cell contains a text constant, and "v" for value if the cell contains anything else. "width" Column width of the cell rounded off to an integer. Each unit of column width is equal to the width of one character in the default font size. Reference the cell that you want information about. If omitted, information specified in info_type is returned for the last cell that was changed. The following list describes the text values CELL returns when info_type is "format", and reference is a cell formatted with a built-in number format. If the Microsoft Excel format is CELL returns General "G" 0 "F0" #,##0 ",0" 0.00 "F2" #,##0.00 ",2" $#,##0_);($#,##0) "C0" $#,##0_);[Red]($#,##0) "C0-" $#,##0.00_);($#,##0.00) "C2" $#,##0.00_);[Red]($#,##0.00) "C2-" 0% "P0" 0.00% "P2" 0.00E+00 "S2" # ?/? or # ??/?? "G" m/d/yy or m/d/yy h:mm or mm/dd/yy "D4" d-mmm-yy or dd-mmm-yy "D1" www.CarltonCollins.com Page 73 Copyright April 2013 Using Excel to Detect Fraud d-mmm or dd-mmm "D2" mmm-yy "D3" mm/dd "D5" h:mm AM/PM "D7" h:mm:ss AM/PM "D6" h:mm "D9" h:mm:ss "D8" If the info type argument in the CELL formula is "format", and if the cell is formatted later with a custom format, then you must recalculate the worksheet to update the CELL formula. Third-Party Solutions – In case Excel’s built in functions are not sufficient to meet your needs, following is a partial list of third-party providers that have products that are used to clean data in a variety of ways. Provider Product Add-in Express Ltd. Add-Ins.com AddinTools CDX Click 2 Convert DigDB JKP Application Development J-Walk & Associates, Inc. Office Assistance LLC PATools PDF2XL Spinnaker Software Solutions Vonnix WinPure ListCleaner Pro Clean and Match 2007 Advanced Find & Replace, Merge Cells Wizard Duplicate Finder AddinTools Assist Zip Stream Converts PDF to Excel formats Add-ins for Excel® Flexfind for Excel Power Utility Pak Version 7 Similar Data Finder for Excel® PATools Advanced Find Replace Converts PDF files to Excel Formats Spinnaker DB tools for Excel Excel Power Expander 4.6 ListCleaner Lite www.CarltonCollins.com Page 74 Copyright April 2013 Using Excel to Detect Fraud Data Cleaning Case Study - Preparing QuickBooks Data When it comes to pivoting QuickBooks data in Excel, you must first do a little bit of clean up work before pivoting process can begin. In the following example, I have exported a QuickBooks general ledger report from 1995 to 2011, and I walk you through the process of preparing the expense portion of this data for pivoting, as follows. 1. Remove Empty Columns – QuickBooks provides an option for exporting a general ledge report to Excel without empty columns, but this is not the default action. If you have not adjusted this default setting, then your exported general ledger report will contain empty columns, and you should remove them by selecting an empty column, right clicking on that cloumn, and selecting Delete Column. Thereafter, you can select each subsequent empty column and press the F4 key to repeat the deletion. 2. Reformat Text Columns – QuickBooks does a nasty thing when it exports data to Excel in that it formats all text columns with text formatting, making it impossible to insert text based formulas, (which we want to do in the next step). To correct this problem, select the text columns and from the Home tab, select General from the Number Format drop down options box in the Number group. 3. Delete the Non Expense Related Rows – In this example, the expense related rows begin on row 9490, therefore I will delete everything above that row, except the top row column headings. Select these rows by clicking on row 9408 (not visible in screen shot), then hold the Shift Key down and press HOME, then move down one row, right-click on the selected range and select DELETE. The data should now appear as follows: 4. Insert Column Descriptions – In this example, there is no column description in Columns A & B, therefore we need to insert descriptions – any description will do. 5. Repeat Account Description – In column B, the Account Description must be repeated on all subsequent blank rows below. For example, the phrase Advertising – Business shown on 2 in the screen image above needs to be repeated on rows 3, 4, 5, and 6. To do this: a. In cell B3, enter the formula =B2. b. Copy cell B3 by pressing Ctrl + C. www.CarltonCollins.com Page 75 Copyright April 2013 Using Excel to Detect Fraud c. Highlight the range from B2 to the end of column B’s data range, B8816 in this example. d. Press the F5 key to launch the GoTo dialog box. e. Click the Special button, check the radio button labeled Blanks, then click OK. f. Paste the data by pressing Ctrl + V. g. Next, select Column B and copy the entire column by pressing Ctrl + C. h. Select Home tab, Paste Special, Paste Values. (Note that without this extra step, you will end up with formulas, and not values throughout column B, which will not be suitable for Pivoting.) 6. Save Your Work – At this point, save your work (and save it often), perhaps to a new file name (such as Export 2), in case power goes out or a major mistake is made. 7. Number Each Row – Insert a new column in front of Column A, and number them sequentially. To insert numbers quickly, enter the number 1 in cell A1 and the number 2 in cell A2. Now highlight cells A1:A2, and doubleclick the Fill handle. This action will automatically number your rows dow to row 8,816. 8. Eliminate Non Transaction Rows – The data’s transaction rows each contain a date, therefore sort the data by the date column, then delete all rows that do not have dates, then resort the data by column A in descending order. www.CarltonCollins.com Page 76 Copyright April 2013 Using Excel to Detect Fraud 9. Delete Columns A & B – Delete columns A & B As they are no longer needed. 10. Format the Data – To make the data visually easier to read, format the data to your desired appearance. For example, I changed all fonts to Calibri and all font colors to black; I removed unneccessaery borders and lines; I bolded the column heading labels only; I adjusted column widths; and I centered certain text columns. My data now appears as follows: 11. Tidy the Debits and Credits Columns – The presentation of the debits and credits columns are not conducive to easy pivoting, therefore I inserted the formula to combine debits and credits in the same column as shown below. Next I converted the formulas contained in Column K to values using the same steps mentioned above (in steps 5g & 5h), labeled Column K, then deleted Columns H, I and J as they are no longer needed. My data now appears as follows: www.CarltonCollins.com Page 77 Copyright April 2013 Using Excel to Detect Fraud 12. Data Review and More Cleaning – Now that the data has been cleaned from a layout point of view, it still must be reviewed for consistency in content as well. For example, in this data set we can see many problems as follows: a. Consistency – A quick review reveals that consistent account descriptins and memos have not been used throughout the data. For example, as shown below the phrase AT&T Universal Card has not been entered consistently. If you plan to summarize data using this phrase, then consistent phrasology is needed throughout. b. No Amounts - Some transactions have no amounts, which may be due to Voided transactions or other explanations. Each of these transactions should be reviewed to determine if that line item should be removed completely. c. Credit Amounts – Some transactions have credit balances, which aren’t expenses at all. Each of these transactions should be reviewed to ensure the data is a valid expense, and if not, consideration should be given to removing the transaction completely. 13. Ready for Pivoting – At this point, the data is ready for pivoting. As an example, the PivotTable report shown below was created in only a few seconds, including time to adjust the grouping of the dates listed across the report in Years. www.CarltonCollins.com Page 78 Copyright April 2013 Using Excel to Detect Fraud www.CarltonCollins.com Page 79 Copyright April 2013 Using Excel to Detect Fraud Looking for Fraud Looking for Fraud in Accounts Payable According to a report to the Nation on Occupational Fraud and Abuse by the Association of Certified Fraud Examiners, losses to fraud in the average company amount to a staggering 6 percent of gross sales. Roughly 45 percent of all fraud involves asset misappropriations of cash in an accounts payable related transaction with an additional 13 percent related to bribery or corruption. This means that 58 percent of 6 percent, or as much as 3.5 percent of gross sales, are lost in this one functional area. Many companies struggle along with profit rates much lower than 3.5 percent! Furthermore, a majority of these misappropriations represent fraudulent vendors, check tampering, and fraudulent expense reimbursements—items that proper controls ought to deal with. It has also been found that companies completing internal or external fraud examinations can reduce their median losses from $153,000 to $87,000 or over 40 percent. Based on these statistics, if a company earns $250 million in sales, they lose an average of 6 percent to fraud from all causes ($15 million) of which roughly 45 percent involves misappropriation of accounts payable ($6.8 million). Of this sum, 40 percent might be saved through internal auditing ($2.7 million). Bottom line, if you can clean up fraud within your accounts payable area, you can make an enormous difference. 1. Above Average Payments to a Vendor - Calculate vendor invoice averages, to locate all invoice amounts exceeding more than twice the vendor’s average. Then scrutinize all vendor payments that exceed twice the average. Use the AVERAGE function and then conditional formatting. 2. Employee to Vendor Address Match - Compare the street address for vendors and employees to see if there are any matches. Look for exact matches and then look for a partial match. Use the IF, MATCH or VLOOKUP functions, along with the LEFT, MID or RIGHT functions. 3. Duplicate Payments - Duplicate payments are sometimes converted to the use of an employee. The employee may notice the duplicate payment, then he or she may prepare a phony endorsement of the check. Looking for Fraud in Revenue Accounts receivable, and the revenue streams that drive it, is arguably the most risky part of a financial statement audit. Any restatement, due to error or fraud, has the potential to bankrupt an organization. According to one study, cash misappropriation fraud (when people who are entrusted to manage an organization’s assets steal those assets) accounts for 30% of all fraud. www.CarltonCollins.com Page 80 Copyright April 2013 Using Excel to Detect Fraud Management is under pressure every day to meet sales and associated income targets. Stock price, management compensation, and analyst/media commitments exacerbate this pressure, so much so that it can persuade management to misstate earnings. Although such misstatements may start as a laissez-faire reading of Generally Accepted Accounting Principles, they tend to snowball into larger and larger entries until they become outright fraud. A 10-year study commissioned by the Committee of Sponsoring Organizations of the Treadway Commission concluded that more than half of frauds involved overstating revenue. This is corroborated by the Association of Certified Fraud Examiner’s report to the Nation on Occupational Fraud and Abuse. If the business model is sound and industry conditions do not pose a threat, there is nothing for management to feel pressured about. Unfortunately, business models are not realized as they are envisioned and the industry may be highly impacted due to change and competition. Management will be the primary identifiers of these trends but may not be compelled to explain such vulnerabilities with investors or creditors. Thus, it is imperative for the fraud examiner to not only understand the business and industry conditions, but also to utilize computer assisted tools to identify trends underlying the financial reports. Aside from financial statement fraud explained above, there are three major types of fraud in the accounts receivable/revenue area: Skimming, in which cash is stolen from an organization before it is recorded on the organization’s books and records. Cash larceny, in which cash is stolen from an organization after it has been recorded on the organization’s books and records. Fraudulent shipments made to employee locations. 4. Missing / Unusual Customer Information - Analyze the Customer records to see if there is missing information. Perhaps use the IF function to pinpoint cells with no data. 5. Uncollected A/R Accounts – Scrutinize all uncollected or severely delinquent A/R balances, and investigate whether these are legitimate customers. If not, search audit trail records to determine who set the customer up in the system. Use the SORT commands or LARGE function to extract transactions with the highest number of days outstanding. 6. Excessive Credit Memos - Similar to excessive voids, this technique can be used to cover the theft of cash. A credit memo to a phony customer is written out, and the cash is taken to make the total cash balance. 7. Write-off of Accounts Receivable - Comparing the write-off of receivables by customers may lead to information indicating that the employee has absconded with customer payments. www.CarltonCollins.com Page 81 Copyright April 2013 Using Excel to Detect Fraud 8. Common Names and Addresses for Refunds - Sales employees frequently make bogus refunds to customers for merchandise. The address shown for the refund is then made to the employee's address, or to the address of a friend or co-worker. 9. Increasing Reconciling Items - Stolen deposits, or bogus checks written, are frequently not removed, or covered, from the reconciliation. Hence, over a period of time, the reconciling items tend to increase. Looking for Fraud in the General Ledger The general ledger is the backbone of the financial records, holding every business transaction. Please note that sub-ledgers (i.e., accounts payable, fixed assets, inventory) may post in full detail (i.e., every accounts payable invoice broken out to the actual detail) but normally this sub-ledger activity is summarized on a periodic basis by account and posted. Regardless, the general ledger is a treasure-trove for fraud reduction. The easiest way to commit financial statement fraud is for a high-ranking officer to post a nonstandard journal entry, falsifying the records. That way, the sub-ledger could show the proper balance but such balance could be adjusted at the general ledger level, with the nonstandard entry. Given this fact, a recent audit standard on fraud (SAS 99 – Consideration of Fraud in a Financial Statement Audit) and later audit risk alerts point to the specific need to review journal entries in the general ledger. 10. Stratify General Ledger Detail Information – Create a report listing the highest debit records and credit records using the LARGE function. 11. Identify Nonstandard Journal Entries Made After Year End – Scrutinize all manual entry records entered after year end and then summarize debits and credits by account. 12. General Ledger Out-of-Balance - When funds, merchandise, or assets are stolen and not covered by a fictitious entry, the general ledger will be out of balance. An inventory of the merchandise or cash is needed to confirm the existence of the missing assets. 13. Adjustments to Receivables or Payables - In cases where customer payments are misappropriated, adjustments to receivables can be made to cover the shortage. Where payables are adjusted, the perpetrator can use a phony billing scheme to convert cash to his or her own use. 14. Looking for Fraud in the Sale of Assets - Some companies use the sale of appreciated assets to hide losses from normal business operations and make the company appear more profitable than it really is, therefore make sure that you investigate any significant decreases in assets to determine if they were sold, then follow the receipts to make sure they have been properly recorded. www.CarltonCollins.com Page 82 Copyright April 2013 Using Excel to Detect Fraud 15. Capitalizing Costs - Capitalizing costs which have no future benefit is one way to enhance current earnings. Make sure all assets are indeed assets with future value, else they need to be written off. 16. Unusual Behavior - The perpetrator will often display unusual behavior, that when taken as a whole is a strong indicator of fraud. The fraudster may not ever take a vacation or call in sick in fear of being caught. He or she may not assign out work even when overloaded. Other symptoms may be changes in behavior such as increased drinking, smoking, defensiveness, and unusual irritability and suspiciousness. 17. Complaints - Frequently tips or complaints will be received which indicate that a fraudulent action is going on. Complaints have been known to be some of the best sources of fraud and should be taken seriously. Although all too often, the motives of the complainant may be suspect, the allegations usually have merit that warrant further investigation. Looking for Fraud in Cash 18. Stale Items in Reconciliations - In bank reconciliations, deposits or checks not included in the reconciliation could be indicative of theft. Missing deposits could mean the perpetrator absconded with the funds; missing checks could indicate one made out to a bogus payee. 19. Excessive Voids - Voided sales slips could mean that the sale was rung up, the payment diverted to the use of the perpetrator, and the sales slip subsequently voided to cover the theft. 20. Missing Documents - Documents which are unable to be located can be a red flag for fraud. Although it is expected that some documents will be misplaced, the auditor should look for explanations as to why the documents are missing, and what steps were taken to locate the requested items. All too often, the auditors will select an alternate item or allow the auditee to select an alternate without determining whether or not a problem exists. www.CarltonCollins.com Page 83 Copyright April 2013 Using Excel to Detect Fraud Looking for Fraud in Payroll 21. Ghost Employees - Ghost employee schemes are frequently uncovered when an auditor, fraud examiner, or other individual distributes paychecks to employees. Missing or otherwise unaccounted for employees could indicate the existence of a ghost employee scheme. 22. Employee Expense Accounts - Employees frequently conceal fraud in their individual expense account reimbursements. These reimbursements should be scrutinized for reasonableness and trends, especially in the area of cash transactions on the expense account. 23. Large Payments to Individuals - Excessively large payments to individuals may indicate instances of fraudulent disbursements. 24. Employee Overtime - Employees being paid for overtime hours not worked by altering time sheets before or after management approval. Looking for Fraud in Inventory 25. Inventory Shortages - Normal shrinkage over a period of time can be computed through historical analysis. Excessive shrinkage could explain a host of fraudulent activity, from embezzlement to theft of inventory. 26. Increased Scrap - In the manufacturing process, an increased amount of scrap could indicate a scheme to steal and resell this material. Scrap is a favorite target of embezzlers because it is usually subject to less scrutiny than regular inventory. 27. Post Office Boxes as Shipping Addresses - In instances where merchandise is shipped to a post office box, this may indicate that an employee is shipping to a bogus purchaser. 28. Excess Purchases - Excess purchases can be used to cover fraud in two ways: 1. Fictitious payees are used to convert funds; and 2. Excessive purchases may indicate a possible payoff of purchasing agent. www.CarltonCollins.com Page 84 Copyright April 2013 Using Excel to Detect Fraud Data Analysis Tools Preparing Data for Data Analysis Before you start to analyze data using Excel’s various data commands such as Sort, Autofilter, Subtotal, Grouping, Consolidate, or PivotTable, you should first inspect your data to determine if it is in Analysis-Ready condition. In general, this means that the data must meet the following criteria: a. Contiguous Data – The data should contain no blank rows or blank columns. For example, the screen below shows blank rows (with solid lines). These rows should first be removed before proceeding with the creation of a PivotTable. b. Single Row Data – Some accounting systems produce data that spans two or more rows per transaction. If this is the case, your will need to clean that data so that all related information for a single transaction or data is contained on a single row. For example, the following data contains multiple rows of data related to a single sales order. In this case, the user must move and paste the data to fall on a single row. This is an example of data that requires a great deal of clean up. www.CarltonCollins.com Page 85 Copyright April 2013 Using Excel to Detect Fraud c. Column Headers - The data should contain a unique header atop each column. For example, the following screen contains two columns labeled Date, while columns D and E contain no heading. These are both cases of data that should be cleaned before creating a PivotTable. If you attempt to analyze data that does not contain a column heading atop all columns, you will sometimes receive an error message, such as the example shown below. If you have data with the same column heading used more than once, Excel will sometimes alter the column headings, for example when you create a PivotTable, so all headings will be unique. www.CarltonCollins.com Page 86 Copyright April 2013 Using Excel to Detect Fraud d. Row Descriptions – Generally, your data should repeat row descriptions for each row. For example, the screen below shows that the state and city descriptions are not repeated for each row in columns A & B. A solutio for quickly filling in the missing row descriptions is presented later in these materials. e. Transposing Headers and Rows – In some cases, data may need to be transposed because many of Excel’s Data tools use the column headings, not the row headings to crunch the data. To do this, copy the data, then select Paste Special, Transpose, OK to flip the data around. f. Clean Data – The data must be clean of empty text cells containing spaces, special characters, extra spaces within data, trailing spaces, trailing zeros, leading zeros, etc. www.CarltonCollins.com Page 87 Copyright April 2013 Using Excel to Detect Fraud Data Analysis Tools Excel provides specialized tools for analyzing data and generating financial reports, yet most CPAs are unaware of these tools or haven’t tried using them before. Specifically useful are the Subtotaling, Grand Totaling, Filtering, Consolidating, Grouping & Outlining, Drilling, OLAP Data Cubes, PivotTables, Sparklines, Data Bar Reporting, Conditional Formatting, Charting, Foot Notes and End Notes, Formula Auditing Tools, Error Checking, Functions, and Data Analysis Tools. The concepts discussed are intended to directly aide the CPA in summarizing, slicing, dicing and analyzing data, and generating related financial reports. 2013 Data Ribbon: 2013 Insert Ribbon: www.CarltonCollins.com Page 88 Copyright April 2013 Using Excel to Detect Fraud Data Sort You would think that every Excel user would already know all about sorting data in Excel, but I am frequently surprised to find that many users have missed a few key points related to using this tool. I don’t mean to belittle you are talk beneath you, but humor me a copy of paragraphs and let’s make sure you are fully up to speed on the following key sorting points: 1. Contiguous Data - The “A to Z” sorting tool can sort a large matrix of data without having to highlight the area as long as the data is contiguous; that is to say that your data should contain no blank columns, no blank rows, and the columns must all be labeled with a column heading. When data is contiguous, all you need to do is place your cursor in a single cell in a given column and click the Sort A to Z or Sort Z to A buttons, and Excel will automatically select the entire matrix for sorting. Surprisingly many users waste a great deal of time highlighting sort ranges prior to sorting, but this step is often unnecessary. 2. A to Z Button - Simply place the cursor in the desired column for sorting, and press the A to Z or Z to A button as the case may be. Excel will automatically sort all continuous columns that have headings and all contiguous rows from the top row under the heading labels down to the last row in the selected column that contains data. (Note - If you accidently select 2 cells instead of just one, your results will not be correct.) 3. Sort by 64 Columns - The “Sort” tool was enhanced beginning in Excel 2007 as it now provides the ability to sort by up to 64 columns, instead of just 3 columns. Presented below is a dialog box which shows this expanded functionality. 4. Sort Left to Right – Excel has always provided the ability to sort left to right. To do so, select the Sort Options box in the Sort dialog box and click the check box labeled Sort Left to Right as pictured below. www.CarltonCollins.com Page 89 Copyright April 2013 Using Excel to Detect Fraud 5. Sort by Color – beginning with Excel 2007, you can also sort by font color or by cell color, or both. This is handy in many ways. Sometimes CPAs use color to tag or mark certain cells - and later find it useful to be able to sort by those markings. In other situations CPAs use conditional formatting to apply color to cells using a wide variety of rules; and thereafter they can sort the data based on the resulting conditional colors. The two sort-by-color options are pictured below. To be fair, it was sort of possible to sort by color in Excel 2003. To accomplish this task, you needed to use the CELL function in order to identify information about a given cell such as the cell color or font color. Thereafter, the results of that function could be used to sort rows – which effectively means that you can sort by color in Excel 2003 – but it takes a bit more effort. 6. Sort By Custom List – Another sorting capability in Excel is the ability to sort by Custom List. For example, assume a CPA firm has ten partners, and the Managing partner prefers to be shown at the top of the list, and the remaining Partners based on seniority. In this case, you could create a Custom List in the Excel Options dialog box listing the partners in the desired order, and then sort future reports based on that order. www.CarltonCollins.com Page 90 Copyright April 2013 Using Excel to Detect Fraud To access the Custom List settings: 1. In Excel 2013 & 2010, select File, Options, Advanced, and scroll to the bottom, and then select Edit Custom List. 2. In Excel 2007, select File, Options, select Edit Custom List option a few inches down from the top. 3. In Excel 2003, select Tools, Options, and click the Custom Lists tab. www.CarltonCollins.com Page 91 Copyright April 2013 Using Excel to Detect Fraud Filtering Data AutoFilter allows you to view a subset of your data and when you are done, you can clear the filters to once again redisplay all of your data. To use this tool, start with any list of data and turn on the AutoFilter tool. Then position your cursor in the column you want to filter and use the drop down arrows to apply your filters as suggested in the screen below. Once the filters are applied, you will see a subset of your data. For example, the screen presented below shows filtered data for only Macon and Savannah properties. As filters are applied, a small funnel icon appears in the drop down arrow button to indicate that a filter has been applied to that particular column. www.CarltonCollins.com Page 92 Copyright April 2013 Using Excel to Detect Fraud Key Points Concerning the AutoFilter Command: 1. Contiguous Data – The AutoFilter tools works best when you are working with data that is contiguous. In other words, your data should contain no blank columns, no blank rows, and the columns must all be labeled. 2. Column Headings –Your columns need unique column headings in and single row, and if the column headings are not in row 1, then the row above the column headings should be blank so Excel will auto detect the correct range. 3. Filter by Multiple Columns - You can filter by more than one column. 4. Filters are Additive - Each additional filter is based on the current filter and further reduces the subset of data. 5. Removing Filters – In all editions of Excel, a fast way to remove multiple filters is to turn AutoFilter off and then turn AutoFilter back on. In Excel 2007 and later editions, you can also click the Clear button in the Sort & Filter Group as pictured below. 6. Filter by Color – You can filter based on colors. For example, you can filter by cell color or by a list of numbers, you can filter by icon or by a custom filter. Note that the Color Filter is mutually exclusive as you cannot also filter by value or text when filter by color is applied, and vice versa. 7. Filters Enabled - A drop-down arrow 8. Filter Applied - A Filter button www.CarltonCollins.com means that filtering is enabled but not applied. means that a filter is applied. Page 93 Copyright April 2013 Using Excel to Detect Fraud 9. Filter Spanning - The commands under the All Dates in the Date Filters menu, such as January or Quarter 2 filter by the period no matter what the year. This can be useful, for example, to compare sales by a period across several years. 10. This Year vs. Year-to-Date - This Year and Year-to-Date are different in the way that future dates are handled. This Year filtering can return dates in the future for the current year, whereas Year-to-Date only returns dates up to and including the current date based on the computer’s time clock. 11. Filtering Dates - All date filters are based on the Gregorian calendar as decreed by Pope Gregory XIII, after whom the calendar was named, on 24 February 1582. The Gregorian calendar modifies the Julian calendar's regular four-year cycle of leap years as follows: Every year that is exactly divisible by four is a leap year, except for years that are exactly divisible by 100; the centurial years that are exactly divisible by 400 are still leap years. For example, the year 1900 is not a leap year; the year 2000 is a leap year. 12. Filtering By Days of Week - If you want to filter by days of the week, simply format the cells to show the day of the week, or insert a new column and use the WEEKDAY function to calculate the week day, and then apply filters using this new column. 13. Top & Bottom Filtering - On the Data tab, in the Sort & Filter group, click Filter. Point to Number Filters and then select Top 10. To filter by number, click Items. To filter by percentage, click Percent. Note - Top and bottom values are based on the original range of cells or table column and not the filtered subset of data. www.CarltonCollins.com Page 94 Copyright April 2013 Using Excel to Detect Fraud 14. Above & Below Average Filtering - On the Data tab, in the Sort & Filter group, click Filter. Select Number Filters, Above/Below Average. Note – These values are based on the original range of cells or table column and not the filtered subset of data. 15. Filtering Out Blanks - To filter out blanks, in the AutoFilter menu at the bottom of the list of values, de-select the check box labeled Blanks. 16. Filtering By Color - Select Filter by Color, and then depending on the type of format, select Filter by Cell Color, Filter by Font Color, or Filter by Cell Icon. Note that these filter options only show up when there are actual cell colors, font colors or icons included in the data range. 17. Filter by Selection - To filter by text, number, date, time, or color for selected cell(s), select the cells to be used as a filter basis and then right-click that selection, and from the popup menu select Filter, Filter by Selected Cell's Value, (or Filter by Selected Cell's Color, Filter by Selected Cell's Font Color, or Filter by Selected Cell's Icon). 18. Refreshing Filters - To reapply a filter after the data changes, click a cell in the range or table, and then on the Data tab, in the Sort & Filter group, click Reapply. www.CarltonCollins.com Page 95 Copyright April 2013 Using Excel to Detect Fraud Data Form Excel’s Data Form tool provides a data input window which makes Excel look and behave more like a database, such as Microsoft Access. (Note that in Excel 2013, 2010 and 2007, the Form tool button has not been included on the Ribbon, so to use it you will first need to add the Form tool button to the Quick Access Toolbar.) A data form provides a convenient means to enter or display one complete row of information in a range or table without scrolling horizontally. Some people, especially those who are used to using databases, find that using a data form can make data entry easier than moving from column to column when you have more columns of data than can be viewed on the screen. Key Points using Data Form: 1. You cannot print data from a data form. 2. Because a data form is a modal dialog box, you cannot use either the Excel Print command or Print button until you close the data form. 3. You might consider using the Windows Print Screen key to make an image of the form, and then paste it into Microsoft Word for printing. www.CarltonCollins.com Page 96 Copyright April 2013 Using Excel to Detect Fraud Data Subtotals Excel’s Subtotal command automatically calculates and inserts subtotals and grand totals in your list or table. Once inserted, Excel recalculates subtotal and grand totals as you enter and edit the detail data. The Subtotal command also outlines the list so that you can display and hide the detail rows for each subtotal. Examples of the Subtotal dialog box and a resulting subtotaled table are shown below. To display subtotals and grand totals at the top instead of the bottom, deselect the checkbox labeled Summary below data. www.CarltonCollins.com Page 97 Copyright April 2013 Using Excel to Detect Fraud Key points to Consider When Using Subtotaling are as follows: 1. Contiguous Data – The Subtotal tools works best when you are working with data that is contiguous. In other words, your data should contain no blank columns, no blank rows, and the columns must all be labeled. 2. Sort Before You Subtotal - You must sort the data by the column you wish to subtotal by, else you will receive erroneous results. 3. Other Mathematical Applications - The Subtotal tool not only calculates subtotals, but it can also calculate minimums, maximums, averages, standard deviations, and other functions. 4. Subtotals in 2013, 2010 & 2007 Tables – Excel 2007 added a new Table tool which enables Subtotals a little differently; the Subtotal tool appears at the bottom of each column in each Table, as shown in the screen below. 5. Automatic Outlining – The Subtotal tool automatically inserts Outlines, which allows you to collapse or expand your data. www.CarltonCollins.com Page 98 Copyright April 2013 Using Excel to Detect Fraud 6. Copying Outline Data - Some CPAs also like to copy and paste collapsed subtotal data to another location, but they find this process copies and pastes all of the data – not just the summary data they desire. In this situation, there are two ways to achieve a clean copy and paste without grabbing all the hidden data as follows: a. CTRL key – Hold the Control Key down while you individually click to select individual rows; this action will enable you to copy and paste selected data. However, this approach can sometimes be problematic because if you miss-click, you have to start over. b. Select Visible Cells – A better approach is to use the Select Visible Cells tool. This tool will select on the data you can see, after which the copy and paste routine will yield the desired results. This option is better because it is faster and less error prone. c. Go To – You can also select visible cells using Go To. To do this, press F5 to launch the Go To tool and then click Special. In the Go To Special dialog box, select the radio button labeled Select Visible cells and press OK. d. ALT + ; - The Alt + ; key combination is the shortcut to using the Select Visible Cells Tool. www.CarltonCollins.com Page 99 Copyright April 2013 Using Excel to Detect Fraud Data Validation Data Validation can be used to limit the data that can be entered into a cell. For example, you might want the user to enter only values between 1% and 99%. You might also use this tool to enable data input to a drop down list which offers two advantages in that it can be faster and more accurate. To create a dropdown list, enter a list into sequentially cells in Excel. Next, from the Data tab select Data Validation, Data Validation (yes, again), then in the dialog box (as shown below) select List from the Allow dropdown box and then indicate the data range for your list in the Source box. After making all the necessary selections in the validation list dialog box, your worksheet will produce a cell containing a drop down list (shown in cell A10 below) that behaves as shown. You can also provide messages to define what input you expect for the cell, and instructions to help users correct any errors. For example, on a worksheet, you can set up a cell to allow only www.CarltonCollins.com Page 100 Copyright April 2013 Using Excel to Detect Fraud account numbers that are exactly three characters long. When users select the cell, you can show them a message such as this one: If users ignore this message and type invalid data in the cell, such as a two-digit or five-digit number, you can display an actual error message. In a more advanced scenario, you might use data validation to calculate the maximum allowed value in a cell based on a value elsewhere in the workbook. In the following example, the user has typed $4,000 in cell E7, which exceeds the maximum limit specified for commissions and bonuses. If the payroll budget were to increase or decrease, the allowed maximum in E7 would automatically increase or decrease with it. www.CarltonCollins.com Page 101 Copyright April 2013 Using Excel to Detect Fraud Data Table (“What-if Analysis”) Data tables are part of the collection of what-if analysis commands, which include: 1. Data Tables 2. Goal Seek 3. Scenarios The Data Table command enables the process of changing values in cells to see how those changes will affect the outcome. For example, you can use a data table to vary the interest rate and term length used in a loan to determine possible monthly payment amounts. There are two types of Data Tables – One Way and Two Way. A data table cannot accommodate more than two variables. If you want to analyze more than two variables, you should use scenarios. Although it is limited to only one or two criterion (one for the row input cell and one for the column input cell), each criterion can include as many different variable values as you want. (In contrast, a Scenario can have a maximum of 32 different criterion, but you can create as many Scenarios as you want.) Loan Analysis Example In this exercise, we start by creating a simple Payment function to calculate the payment amount of a loan given a loan amount, interest rate and number of periods. The next step is to create a Two-Way Data Table displaying the resulting payment amount given a variety of lengths of the loan. This process is started by creating a list of the alternative loan amounts, as shown below in B8, B9, B10, etc. Cell C7 must reference the results you want to be displayed in the table. www.CarltonCollins.com Page 102 Copyright April 2013 Using Excel to Detect Fraud Next, highlight the data table range and use the Data Table command on the Data tab (as shown below) to generate the desired table. This process will generate the following table: This table tells us that the same loan amount will require a monthly payment of $4,972 to pay the loan off in just 6 years, or a monthly payment of $5,800 to repay the loan in just 5 years. www.CarltonCollins.com Page 103 Copyright April 2013 Using Excel to Detect Fraud The next step in this exercise is to generate a line chart based on the data table we just created. This line chart will provide some interesting observations regarding the benefits and detriments of paying off loans over longer periods. The resulting chart is shown as follows: www.CarltonCollins.com Page 104 Copyright April 2013 Using Excel to Detect Fraud Based on this, no one should ever obtain a fair market loan for more than 15 years, the reduction in payments simply aren’t worth the additional length of the loan. This same basic behavior is seen whether the interest rate is 1% or 100%, or whether the loan amount is $1,000 or $10,000,000. The only time you might be justified in obtaining a loan longer than 15 years might be when you are extended a favorable interest rate (perhaps from a rich uncle), better than a fair market interest rate. Goal Seek If you know the result that you want from a formula, but are not sure what input values are needed to produce your desired results, use Goal Seek. For example, suppose that you have decided to purchase a house, but you don’t know how much house you can afford. In this case, know how the interest rate (3.75%) and how long you want to take to pay off the loan (15 years), and the amount you can afford to pay each month ($2,800). In this case, you can use Goal Seek to work backwards to figure out how much house you can afford. Start by calculating the monthly payment based on any random home loan amount as pictured below. Next, from the Data tab, select What-If Analysis, Goal Seek. Fill in the parameters to set the payment amount to $2800 by adjusting the Loan Amount, as shown, and then click OK. The result is that a person with $2,800 available to make monthly payments can afford to purchase a home costing up to $385,027 (assuming a 15 year loan and 3.75% interest rate) – as pictured above. (Keep in mind that anyone actually following this scenario would need to consider that homes also come with other monthly obligations including real estate taxes, insurance maintenance, etc.) www.CarltonCollins.com Page 105 Copyright April 2013 Using Excel to Detect Fraud Data - Text to Columns CPAs sometimes receive data from their clients or IT departments that is in text form. When this happens, Excel can split the contents of one or more cells in a column and distribute those contents as individual parts across other cells in adjacent columns. For example, the worksheet below contains a column of full names and amounts that you want to split into separate columns. The Text to Columns wizard parses the data automatically into separate cells. To use this tool, select the cell, range or entire column that contains the text values that you want to split. Notes: 1. A range that you want to split can include any number of rows, but it can include no more than one column. 2. You also should make sure there are enough blank columns to the right of the selected column to prevent overwriting existing data in those adjacent columns. www.CarltonCollins.com Page 106 Copyright April 2013 Using Excel to Detect Fraud Flash Fill Of all the Office 2013 applications, Excel is the beneficiary of the most impressive enhancements. Excel’s new Flash Fill watches you work and applies logic to help you complete your tasks. The example pictured below contains a list of 44 first and last names in Column A, which I want to separate into Columns B and C. As I start typing the first name of the second record in Column B; Excel’s Flash Fill guesses what I’m trying to do and offers to fill in the remaining 42 first names (as shown in grey text). www.CarltonCollins.com Page 107 Copyright April 2013 Using Excel to Detect Fraud Data Grouping & Outlining If you have a list of data that you want to group and summarize, you can create an outline of up to eight levels. Each inner level (represented by a higher number in the outline symbols) displays detailed data for the preceding outer level, represented by a lower number in the outline symbols. Use an outline to quickly display summary rows or columns, or to reveal the detail data for each group. You can create an outline of rows (as shown in the example below), an outline of columns, or an outline of both rows and columns. www.CarltonCollins.com Page 108 Copyright April 2013 Using Excel to Detect Fraud PowerView Excel’s new PowerView inserts new worksheets connected to your data, and then enables you to create new report types, such as the interactive map chart presented below. The resulting PowerView Map report is zoomable, and filters can be applied to display partial data. PowerView worksheets can be published as standalone, interactive reports to Microsoft SharePoint’s PowerPivot Gallery or other reporting service destinations. Some of the tools provided by PowerView include the ability to create a dashboard containing multiple PowerViews, apply themes and backgrounds, insert pictures and text boxes, insert collapsible and expandable tiles, and add data slicers. PowerView Learning Points 1. Included - PowerView isn’t included in Office Home editions. Power View and PowerPivot are only available in the Office Professional Plus and Office 365 Professional Plus editions. 2. Worksheet - PowerView is another sheet in the workbook, and acts like a Dashboard. 3. Fields – Add data to the PowerView by selecting fields, much like you do for PivotTables. 4. Play - You can play charts to see how they change over time. 5. PowerView uses PowerPivot - Known to be extremely fast for retrieving and sorting data. 6. Relationships – PowerView can integrate multiple data sets via relationships. www.CarltonCollins.com Page 109 Copyright April 2013 Using Excel to Detect Fraud Timeline Slicer CPAs who work with PivotTables will likely appreciate Excel’s new Timeline Slicer which helps users slice and dice Pivot data that contain dates. As an example, selecting the dates May through October on the Timeline slicer pictured below adjusts the PivotTable to display May thru October data. Quick Analysis Excel’s Quick Analysis tool also helps you analyze data by offering a variety of formatting, charts, totals, tables and sparkline layouts to instantly summarize large volumes of data (see screen below). When using Quick Analysis to scrutinize text-only data, text specific options for highlighting duplicate or unique text items appear. www.CarltonCollins.com Page 110 Copyright April 2013 Using Excel to Detect Fraud Queries Excel 2010, 2007 & 2003 include pre-designed “queries” that can import commonly used data such as stock quotes for updating a stock portfolio. All you need is a connection to the internet and of course, some stock ticker symbols. In Excel 2010 or 2007 select Data, Existing Connections, MSN MoneyCentral Stock Quotes (or in Excel 2003 select Data, Import External Data, Import Data Existing Connections, MSN MoneyCentral Stock Quotes) and then walk through the web query wizard for importing stock quotes. In just a few seconds, Excel will retrieve Real-Time data for NYSE, NASDAQ & AMEX, and 20 minute delayed stock prices from other exchanges (during the hours when the stock market is open) and display a grid of complete upto-date stock price information that is synchronized to the stock market’s changing stock prices. With each click of the “Refresh” button, the stock price information in Excel is updated - this sure beats picking numbers out of the newspaper. Completing the Stock Portfolio – Next link the grid data to another worksheet, and insert new columns containing the number of shares owned, as wells as an additional column to compute the total value based on shares owned, as shown below. www.CarltonCollins.com Page 111 Copyright April 2013 Using Excel to Detect Fraud Refreshing the Stock Prices - Once you have created your portfolio, simply click the Refresh Data button on the “External Data” Toolbar in Excel 2003 or on the “Data Ribbon” in Excel 2010 & 2007 shown below to update the current value of your Portfolio. Query Parameters - There are numerous options to help you extract exactly the data you want the way you want it. The “Web Query Parameters Box”, “Web Query Options box” and “External Data Properties Box” provide numerous options for controlling your web query. Excel 2013 Stock Quote Queries In Excel 2013, for unknown reasons Microsoft has removed the stock quote query option, therefore below are instructions for restoring this option. 1. Launch Notepad (Start, Programs, Windows Accessories, Notepad) 2. Enter the following information exactly: Web 1 http://moneycentral.msn.com/investor/external/excel/quotes.asp?SYMBOL=["QUOTE","Ent er stock, fund or other MSN MoneyCentral Investor symbols separated by commas."] Or if you prefer, use this to query Yahoo’s stock prices: www.CarltonCollins.com Page 112 Copyright April 2013 Using Excel to Detect Fraud 3. Save the file using any name you want, but be sure to include the extension .igy as pictured. 4. Make sure to save this file to the folder labeled My Data Sources. 5. Now in Excel, from the Data tab select Existing Queries, then scroll to and launch the new query you just created – it should work just like it did in Excel 2010, 2007 and 2003. www.CarltonCollins.com Page 113 Copyright April 2013 Using Excel to Detect Fraud Database Queries Microsoft Excel can also query and retrieve data you want from an external data source. For example, you can retrieve Microsoft Excel data about a specific product by region. You can create a simple query by using the Query Wizard, or you can create a more complex query by using the advanced features of Microsoft Query. To use Microsoft Query to retrieve external data, you must: 1. Have access to an external data source - If the data is not on your local computer, you may need to see the administrator of the external database for a password, user permission, or other information about how to connect to the database. 2. Install Microsoft Query - If Microsoft Query is not available, you might need to install it. 3. Specify a source to retrieve data from, and then start using Microsoft Query - For example, if you want to insert database information, display the Database toolbar, click Insert Database, click Get Data, and then click MS Query. For example, suppose we have some data in our accounting system – Sage MAS 200 ERP that we would like to analyze in Excel. We can use the Database Query Wizard to build a query that will extract the data we need and place it in an Excel spreadsheet, as follows. www.CarltonCollins.com Page 114 Copyright April 2013 Using Excel to Detect Fraud The first step is to select the type of database you want to query and to select the specific database. Upon the selection of the desired database a list of tables will be presented. Choose the desired tables, and select the desired data fields to be imported. You will then have the option to filter and sort the data before it is imported. Finally you will be given the option to save the query so you can run it at a later date without having to start from scratch. Excel will then return a table full of the data you requested as shown in the screen below. www.CarltonCollins.com Page 115 Copyright April 2013 Using Excel to Detect Fraud www.CarltonCollins.com Page 116 Copyright April 2013 Using Excel to Detect Fraud Sparklines Sparklines – Sparklines are small cell-sized charts that you can embed in a worksheet next to data to get a quick visual representation of the data. For example, if you had a worksheet that tracked the performance of several dozen stocks, you could create a Sparkline for each stock that graphed its performance over time, in a very compact way. Here are examples: Conditional Formatting 1. Conditional Formatting Improvements – Microsoft has improved and added more styles and icons regarding the ability to apply a format to a range of cells, and then have the formatting change according to the value of the cell or formula. 2. Solid Bars - Data Bars can be solid now in Excel 2010. Excel 2007 bars offered only a gradient effect, which was visually was confusing to read. Below is a comparison on the Excel 2007 and Excel 2010 Data Bar options. 2007 Gradient 2010 Gradient 2010 Solid 3. Negative Numbers - Microsoft corrected a problem which Excel 2007 had when creating Data Bars based on negative numbers, by adding axis support for both positive and www.CarltonCollins.com Page 117 Copyright April 2013 Using Excel to Detect Fraud negative values. The screens shots below show Excel 2010’s new solution, and how Excel 2007 got confused when applying Data Bars to the exact same data. Excel 2010’s Data Bars Excel 2007’s Data Bars a. More Data Bar Options - Notice that Microsoft added more Data Bar options as shown in the comparison below. Excel 2010’s Data Bar Menu www.CarltonCollins.com Page 118 Excel 2007’s Data Bar Menu Copyright April 2013 Using Excel to Detect Fraud b. More Icon Options - Notice that Microsoft added more Data Bar options as shown in the comparison below. Excel 2010’s Icon Menu Excel 2007’s Icon Menu c. Arrow Colors - It is possible to change icons used for KPI i.e. You can have two arrows with different colors that might represent both a negative impact and positive growth, for example. d. Referring to Data - You can now refer to data on different worksheets, and even refer to a range outside the Conditional Formatting area. The screen below shows the error www.CarltonCollins.com Page 119 Copyright April 2013 Using Excel to Detect Fraud message produced by Excel 2007 when you attempted to reference cells outside the Conditional Formatting area; Excel 2010 now accommodates this situation. Excel 2007 Error Message 4. Sort by Color – Beginning with 2007, Excel provides the ability to sort by font color or by cell color, or both. This is handy in many ways. Sometimes CPAs use color to tag or mark certain cells - and later find it useful to be able to sort by those markings. In other situations CPAs use conditional formatting to apply color to cells using a wide variety of rules. Thereafter Excel can sort the data based on the resulting colors. The sort-by-color options are shown below. To be accurate, it was possible to sort by color in Excel 2003. To accomplish this task, you needed to use the =CELL function in order to identify information about a given cell such as the cell color or font color. Thereafter, the results of that function could be used to sort rows – which effectively means that you can sort by color in Excel 2003 – but it takes a bit more effort. www.CarltonCollins.com Page 120 Copyright April 2013 Using Excel to Detect Fraud Excel Functions An Excel function is a preset formula that calculates a specific result based on the criteria/variables/arguments you specify. All functions start with the equal sign followed by the function’s name and criteria/variables/arguments you specify. As a simple example, the most frequently used function in Excel is the SUM function, which is used to add data. There are a total of 455 Excel Functions in Excel 2013; the following table summarizes the number of functions introduced in previous editions of Excel. Excel Functions are preprogrammed formulas that make the task of writing complex formulas easier. There are a total of 455 functions in Excel. These functions are separated into 14 categories as follows: www.CarltonCollins.com Page 121 Copyright April 2013 Using Excel to Detect Fraud Categories & Number Of Functions In Each Category 1. Compatibility .................................................................. (38) 2. Cubes ................................................................................ (7) 3. Databases ....................................................................... (12) 4. Date and times ............................................................... (24) 5. Engineering .................................................................... (54) 6. Financials ........................................................................ (55) 7. Information .................................................................... (20) 8. Logical .............................................................................. (9) 9. Lookup and references .................................................. (19) 10. Math and trigonometry ................................................. (79) 11. Statistical ...................................................................... (101) 12. Texts ............................................................................... (30) 13. User defined that are installed with add-ins ................... (4) 14. Webs ................................................................................ (3) Function Relevance to CPAs Some Excel functions are more powerful than others and some are more relevant to the CPA than others. For example, most CPAs will find the IF, SUM, COUNT, SUBTOTAL, TEXT, and VLOOKUP are very relevant to the CPA while other engineering and trigonometry functions such as LOG, PI, RADIENS, DELTA, TAN, COMPLEX, and HAX2DEC are typically less relevant to CPAs. It has been my experience that approximately 171 functions are more relevant or important to CPAs; therefore in my opinion, CPAs wishing to increase their command of Excel functions should concentrate on learning these functions primarily. To help you accomplish this goal, presented below is a list of all 455 Excel functions, along with a brief explanation of each function. The 170 functions that I find more relevant are shown in red bold. Compatibility 1 2 BETADIST BETAINV 3 4 5 BINOMDIST CHIDIST CHIINV 6 7 8 CHITEST CONFIDENCE COVAR www.CarltonCollins.com Returns the beta cumulative distribution Returns the inverse of the cumulative distribution for a specified beta distribution Returns the individual term binomial distribution probability Returns the one-tailed probability of the chi-squared distribution Returns the inverse of the one-tailed probability of the chi-squared distribution Returns the test for independence Returns the confidence interval for a population mean Returns covariance, the avg of the products of paired deviations Page 122 Copyright April 2013 Using Excel to Detect Fraud 9 CRITBINOM Returns the smallest value for which the cumulative binomial distribution is less than or equal to a criterion value 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 EXPONDIST FDIST FINV FTEST GAMMADIST GAMMAINV HYPGEOMDIST LOGINV LOGNORMDIST MODE NEGBINOMDIST NORMDIST NORMINV NORMSDIST NORMSINV PERCENTILE PERCENTRANK POISSON QUARTILE RANK STDEV STDEVP TDIST TINV TTEST VAR VARP WEIBULL ZTEST Returns the exponential distribution Returns the F probability distribution Returns the inverse of the F probability distribution Returns the result of an F-test Returns the gamma distribution Returns the inverse of the gamma cumulative distribution Returns the hypergeometric distribution Returns the inverse of the lognormal cumulative distribution Returns the cumulative lognormal distribution Returns the most common value in a data set Returns the negative binomial distribution Returns the normal cumulative distribution Returns the inverse of the normal cumulative distribution Returns the standard normal cumulative distribution Returns the inverse of the standard normal cumulative distribution Returns the k-th percentile of values in a range Returns the percentage rank of a value in a data set Returns the Poisson distribution Returns the quartile of a data set Returns the rank of a number in a list of numbers Estimates standard deviation based on a sample Calculates standard deviation based on the entire population Returns the Student's t-distribution Returns the inverse of the Student's t-distribution Returns the probability associated with a Student's t-test Estimates variance based on a sample Calculates variance based on the entire population Returns the Weibull distribution Returns the one-tailed probability-value of a z-test 39 CUBEKPIMEMBER 40 CUBEMEMBER Returns a key performance indicator (KPI) property and displays the KPI name in the cell. A KPI is a quantifiable measurement, such as monthly gross profit or quarterly employee turnover, that is used to monitor an organization's performance. Returns a member or tuple from the cube. Use to validate that the member or tuple exists in the cube. Cubes www.CarltonCollins.com Page 123 Copyright April 2013 Using Excel to Detect Fraud 41 CUBEMEMBERPROPER TY 42 CUBERANKEDMEMBER 43 CUBESET 44 45 CUBESETCOUNT CUBEVALUE Returns the value of a member property from the cube. Use to validate that a member name exists within the cube and to return the specified property for this member. Returns the nth, or ranked, member in a set. Use to return one or more elements in a set, such as the top 10 sales performers. Defines a calculated set of members or tuples by sending a set expression to the cube on the server, which creates the set, and then returns that set to Microsoft Office Excel. Returns the number of items in a set. Returns an aggregated value from the cube. Databases 46 47 48 49 DAVERAGE DCOUNT DCOUNTA DGET 50 51 52 DMAX DMIN DPRODUCT 53 54 DSTDEV DSTDEVP 55 DSUM 56 57 DVAR DVARP Returns the average of selected database entries Counts the cells that contain numbers in a database Counts nonblank cells in a database Extracts from a database a single record that matches the specified criteria Returns the maximum value from selected database entries Returns the minimum value from selected database entries Multiplies the values in a particular field of records that match the criteria in a database Estimates standard deviation based on a sample of the database Calculates the standard deviation based on the entire population of selected database entries Adds the numbers in the field column of records in the database that match the criteria Estimates variance based on a sample of the database Calculates variance based on the entire population of selected database entries Date and Time 58 59 60 61 62 DATE DATEVALUE DAY DAYS DAYS360 63 EDATE 64 EOMONTH 65 HOUR www.CarltonCollins.com Returns the serial number of a particular date Converts a date in the form of text to a serial number Converts a serial number to a day of the month Returns the number of days between two dates Calculates the number of days between two dates based on a 360day year Returns the serial number of the date that is the indicated number of months before or after the start date Returns the serial number of the last day of the month before or after a specified number of months Converts a serial number to an hour Page 124 Copyright April 2013 Using Excel to Detect Fraud 66 ISOWEEKNUM 67 68 69 70 MINUTE MONTH NETWORKDAYS NETWORKDAYS.INTL 71 72 73 74 75 76 77 NOW SECOND TIME TIMEVALUE TODAY WEEKDAY WEEKNUM 78 WORKDAY 79 WORKDAY.INTL 80 81 YEAR YEARFRAC Returns the number of the ISO week number of the year for a given date Converts a serial number to a minute Converts a serial number to a month Returns the number of whole workdays between two dates Returns the number of whole workdays between two dates using parameters to indicate how many days are weekend days Returns the serial number of the current date and time Converts a serial number to a second Returns the serial number of a particular time Converts a time in the form of text to a serial number Returns the serial number of today's date Converts a serial number to a day of the week Converts a serial number to a number representing where the week falls numerically with a year Returns the serial number of the date before or after a specified number of workdays Returns the serial number of the date before or after a specified number of workdays using parameters to indicate which and how many days are weekend days Converts a serial number to a year Returns the year fraction representing the number of whole days between start_date and end_date Engineering 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 BESSELI BESSELJ BESSELK BESSELY BIN2DEC BIN2HEX BIN2OCT BITAND BITLSHIFT BITOR BITRSHIFT BITXOR COMPLEX CONVERT DEC2BIN DEC2HEX www.CarltonCollins.com Returns the modified Bessel In(x) Returns the Bessel Jn(x) Returns the modified Bessel Kn(x) Returns the Bessel Yn(x) Converts a binary number to decimal Converts a binary number to hexadecimal Converts a binary number to octal Returns a 'Bitwise And' of two numbers Returns a value number shifted left by shift_amount bits Returns a bitwise OR of 2 numbers Returns a value number shifted right by shift_amount bits Returns a bitwise 'Exclusive Or' of two numbers Converts real and imaginary coefficients into a complex number Converts a number from one measurement system to another Converts a decimal number to binary Converts a decimal number to hexadecimal Page 125 Copyright April 2013 Using Excel to Detect Fraud 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 DEC2OCT DELTA ERF ERF.PRECISE ERFC ERFC.PRECISE GESTEP HEX2BIN HEX2DEC HEX2OCT IMABS IMAGINARY IMARGUMENT IMCONJUGATE IMCOS IMCOSH IMCOT IMCSC IMCSCH IMDIV IMEXP IMLN IMLOG10 IMLOG2 IMPOWER IMPRODUCT IMREAL IMSEC IMSECH IMSIN IMSINH IMSQRT IMSUB IMSUM IMTAN OCT2BIN OCT2DEC OCT2HEX www.CarltonCollins.com Converts a decimal number to octal Tests whether two values are equal Returns the error Returns the error Returns the complementary error Returns the complementary ERF integrated between x and infinity Tests whether a number is greater than a threshold value Converts a hexadecimal number to binary Converts a hexadecimal number to decimal Converts a hexadecimal number to octal Returns the absolute value (modulus) of a complex number Returns the imaginary coefficient of a complex number Returns the argument theta, an angle expressed in radians Returns the complex conjugate of a complex number Returns the cosine of a complex number Returns the hyperbolic cosine of a complex number Returns the cotangent of a complex number Returns the cosecant of a complex number Returns the hyperbolic cosecant of a complex number Returns the quotient of two complex numbers Returns the exponential of a complex number Returns the natural logarithm of a complex number Returns the base-10 logarithm of a complex number Returns the base-2 logarithm of a complex number Returns a complex number raised to an integer power Returns the product of from 2 to 255 complex numbers Returns the real coefficient of a complex number Returns the secant of a complex number Returns the hyperbolic secant of a complex number Returns the sine of a complex number Returns the hyperbolic sine of a complex number Returns the square root of a complex number Returns the difference between two complex numbers Returns the sum of complex numbers Returns the tangent of a complex number Converts an octal number to binary Converts an octal number to decimal Converts an octal number to hexadecimal Page 126 Copyright April 2013 Using Excel to Detect Fraud Financial 136 137 138 ACCRINT ACCRINTM AMORDEGRC 139 140 AMORLINC COUPDAYBS 141 COUPDAYS 142 COUPDAYSNC 143 144 COUPNCD COUPNUM 145 146 147 148 COUPPCD CUMIPMT CUMPRINC DB 149 DDB 150 151 DISC DOLLARDE 152 DOLLARFR 153 DURATION 154 155 156 EFFECT FV FVSCHEDULE 157 158 159 160 INTRATE IPMT IRR ISPMT 161 MDURATION 162 MIRR 163 NOMINAL www.CarltonCollins.com Returns accrued interest for a security paying periodic interest Returns accrued interest for a security paying interest at maturity Returns the depreciation for each accounting period by using a depreciation coefficient Returns the depreciation for each accounting period Returns the number of days from the beginning of the coupon period to the settlement date Returns the number of days in the coupon period that contains the settlement date Returns the number of days from the settlement date to the next coupon date Returns the next coupon date after the settlement date Returns the number of coupons payable between the settlement date and maturity date Returns the previous coupon date before the settlement date Returns the cumulative interest paid between two periods Returns cumulative principal paid on a loan between two periods Returns the depreciation of an asset for a specified period by using the fixed-declining balance method Returns the depreciation of an asset for a specified period by using the double-declining balance method or some other method that you specify Returns the discount rate for a security Converts a dollar price, expressed as a fraction, into a dollar price, expressed as a decimal number Converts a dollar price, expressed as a decimal number, into a dollar price, expressed as a fraction Returns the annual duration of a security with periodic interest payments Returns the effective annual interest rate Returns the future value of an investment Returns the future value of an initial principal after applying a series of compound interest rates Returns the interest rate for a fully invested security Returns interest payment for an investment for a given period Returns the internal rate of return for a series of cash flows Calculates the interest paid during a specific period of an investment Returns the Macauley modified duration for a security with an assumed par value of $100 Returns the internal rate of return where positive and negative cash flows are financed at different rates Returns the annual nominal interest rate Page 127 Copyright April 2013 Using Excel to Detect Fraud 164 165 NPER NPV Returns the number of periods for an investment Returns the net present value of an investment based on a series of periodic cash flows and a discount rate 166 ODDFPRICE 167 168 ODDFYIELD ODDLPRICE 169 170 ODDLYIELD PDURATION 171 172 PMT PPMT 173 PRICE 174 175 PRICEDISC PRICEMAT 176 177 178 PV RATE RECEIVED 179 RRI 180 181 SLN SYD 182 183 184 185 TBILLEQ TBILLPRICE TBILLYIELD VDB Returns price per $100 face value of a security with an odd first period Returns the yield of a security with an odd first period Returns the price per $100 face value of a security with an odd last period Returns the yield of a security with an odd last period Returns the number of periods required by an investment to reach a specified value Returns the periodic payment for an annuity Returns the payment on the principal for an investment for a given period Returns the price per $100 face value of a security that pays periodic interest Returns the price per $100 face value of a discounted security Returns the price per $100 face value of a security that pays interest at maturity Returns the present value of an investment Returns the interest rate per period of an annuity Returns the amount received at maturity for a fully invested security Returns an equivalent interest rate for the growth of an investment Returns the straight-line depreciation of an asset for one period Returns the sum-of-years' digits depreciation of an asset for a specified period Returns the bond-equivalent yield for a Treasury bill Returns the price per $100 face value for a Treasury bill Returns the yield for a Treasury bill Returns the depreciation of an asset for a specified or partial period by using a declining balance method 186 XIRR 187 XNPV 188 189 YIELD YIELDDISC 190 YIELDMAT Returns the internal rate of return for a schedule of cash flows that is not necessarily periodic Returns the net present value for a schedule of cash flows that is not necessarily periodic Returns the yield on a security that pays periodic interest Returns the annual yield for a discounted security; for example, a Treasury bill Returns the annual yield of a security that pays interest at maturity Informational www.CarltonCollins.com Page 128 Copyright April 2013 Using Excel to Detect Fraud 191 CELL 192 193 ERROR.TYPE INFO 194 195 196 197 198 ISBLANK ISERR ISERROR ISEVEN ISFORMULA 199 200 201 202 203 204 205 206 207 208 209 210 ISLOGICAL ISNA ISNONTEXT ISNUMBER ISODD ISREF ISTEXT N NA SHEET SHEETS TYPE Returns information about the formatting, location, or contents of a cell Note This is not available in Excel Web App. Returns a number corresponding to an error type Returns information about the current operating environment. Note This is not available in Excel Web App. Returns TRUE if the value is blank Returns TRUE if the value is any error value except #N/A Returns TRUE if the value is any error value Returns TRUE if the number is even Returns TRUE if there is a reference to a cell that contains a formula Returns TRUE if the value is a logical value Returns TRUE if the value is the #N/A error value Returns TRUE if the value is not text Returns TRUE if the value is a number Returns TRUE if the number is odd Returns TRUE if the value is a reference Returns TRUE if the value is text Returns a value converted to a number Returns the error value #N/A Returns the sheet number of the referenced sheet Returns the number of sheets in a reference Returns a number indicating the data type of a value Logical 211 212 213 214 AND FALSE IF IFERROR Returns TRUE if all of its arguments are TRUE Returns the logical value FALSE Specifies a logical test to perform Returns a value you specify if a formula evaluates to an error; otherwise, returns the result of the formula 215 IFNA Returns the value you specify if the expression resolves to #N/A, otherwise returns the result of the expression 216 217 218 219 NOT OR TRUE XOR Reverses the logic of its argument Returns TRUE if any argument is TRUE Returns the logical value TRUE Returns a logical exclusive OR of all arguments Lookup and Reference 220 221 ADDRESS AREAS www.CarltonCollins.com Returns a reference as text to a single cell in a worksheet Returns the number of areas in a reference Page 129 Copyright April 2013 Using Excel to Detect Fraud 222 223 224 225 226 227 CHOOSE COLUMN COLUMNS FORMULATEXT GETPIVOTDATA HLOOKUP 228 HYPERLINK 229 230 231 232 233 234 235 236 INDEX INDIRECT LOOKUP MATCH OFFSET ROW ROWS RTD 237 238 TRANSPOSE VLOOKUP Chooses a value from a list of values Returns the column number of a reference Returns the number of columns in a reference Returns the formula at the given reference as text Returns data stored in a PivotTable report Looks in the top row of an array and returns the value of the indicated cell Creates a shortcut or jump that opens a document stored on a network server, an intranet, or the Internet Uses an index to choose a value from a reference or array Returns a reference indicated by a text value Looks up values in a vector or array Looks up values in a reference or array Returns a reference offset from a given reference Returns the row number of a reference Returns the number of rows in a reference Retrieves real-time data from a program that supports COM automation (Automation: A way to work with an application's objects from another application or development tool. Formerly called OLE Automation, Automation is an industry standard and a feature of the Component Object Model (COM).) Returns the transpose of an array Looks in the first column of an array and moves across the row to return the value of a cell Math & Trigonometry 239 240 241 242 243 244 245 246 247 248 249 250 251 ABS ACOS ACOSH ACOT ACOTH AGGREGATE ARABIC ASIN ASINH ATAN ATAN2 ATANH BASE www.CarltonCollins.com Returns the absolute value of a number Returns the arccosine of a number Returns the inverse hyperbolic cosine of a number Returns the arccotangent of a number Returns the hyperbolic arccotangent of a number Returns an aggregate in a list or database Converts a Roman number to Arabic, as a number Returns the arcsine of a number Returns the inverse hyperbolic sine of a number Returns the arctangent of a number Returns the arctangent from x- and y-coordinates Returns the inverse hyperbolic tangent of a number Converts a number into a text representation with the given radix (base) Page 130 Copyright April 2013 Using Excel to Detect Fraud 252 CEILING 253 CEILING.MATH 254 CEILING.PRECISE 255 256 COMBIN COMBINA 257 258 259 260 261 262 263 COS COSH COT COTH CSC CSCH DECIMAL 264 265 266 267 268 269 270 DEGREES EVEN EXP FACT FACTDOUBLE FLOOR FLOOR.MATH 271 FLOOR.PRECISE 272 273 274 GCD INT ISO.CEILING 275 276 277 278 279 280 281 282 283 LCM LN LOG LOG10 MDETERM MINVERSE MMULT MOD MROUND www.CarltonCollins.com Rounds a number to the nearest integer or to the nearest multiple of significance Rounds a number up, to the nearest integer or to the nearest multiple of significance Rounds a number the nearest integer or to the nearest multiple of significance. Regardless of the sign of the number, the number is rounded up. Returns the number of combinations for a given number of objects Returns the number of combinations with repetitions for a given number of items Returns the cosine of a number Returns the hyperbolic cosine of a number Returns the cotangent of an angle Returns the hyperbolic cotangent of a number Returns the cosecant of an angle Returns the hyperbolic cosecant of an angle Converts a text representation of a number in a given base into a decimal number Converts radians to degrees Rounds a number up to the nearest even integer Returns e raised to the power of a given number Returns the factorial of a number Returns the double factorial of a number Rounds a number down, toward zero Rounds a number down, to the nearest integer or to the nearest multiple of significance Rounds a number down to the nearest integer or to the nearest multiple of significance. Regardless of the sign of the number, the number is rounded down. Returns the greatest common divisor Rounds a number down to the nearest integer Returns a number that is rounded up to the nearest integer or to the nearest multiple of significance Returns the least common multiple Returns the natural logarithm of a number Returns the logarithm of a number to a specified base Returns the base-10 logarithm of a number Returns the matrix determinant of an array Returns the matrix inverse of an array Returns the matrix product of two arrays Returns the remainder from division Returns a number rounded to the desired multiple Page 131 Copyright April 2013 Using Excel to Detect Fraud 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 MULTINOMIAL MUNIT ODD PI POWER PRODUCT QUOTIENT RADIANS RAND RANDBETWEEN ROMAN ROUND ROUNDDOWN ROUNDUP SEC SECH SERIESSUM SIGN SIN SINH SQRT SQRTPI SUBTOTAL SUM SUMIF SUMIFS SUMPRODUCT 311 312 SUMSQ SUMX2MY2 313 SUMX2PY2 314 SUMXMY2 315 316 317 TAN TANH TRUNC Returns the multinomial of a set of numbers Returns the unit matrix or the specified dimension Rounds a number up to the nearest odd integer Returns the value of pi Returns the result of a number raised to a power Multiplies its arguments Returns the integer portion of a division Converts degrees to radians Returns a random number between 0 and 1 Returns a random number between the numbers you specify Converts an Arabic numeral to Roman, as text Rounds a number to a specified number of digits Rounds a number down, toward zero Rounds a number up, away from zero Returns the secant of an angle Returns the hyperbolic secant of an angle Returns the sum of a power series based on the formula Returns the sign of a number Returns the sine of the given angle Returns the hyperbolic sine of a number Returns a positive square root Returns the square root of (number * pi) Returns a subtotal in a list or database Adds its arguments Adds the cells specified by a given criteria Adds the cells in a range that meet multiple criteria Returns the sum of the products of corresponding array components Returns the sum of the squares of the arguments Returns the sum of the difference of squares of corresponding values in two arrays Returns the sum of the sum of squares of corresponding values in two arrays Returns the sum of squares of differences of corresponding values in two arrays Returns the tangent of a number Returns the hyperbolic tangent of a number Truncates a number to an integer Statistical www.CarltonCollins.com Page 132 Copyright April 2013 Using Excel to Detect Fraud 318 AVEDEV 319 320 AVERAGE AVERAGEA 321 AVERAGEIF 322 AVERAGEIFS 323 324 BETA.DIST BETA.INV 325 326 BINOM.DIST BINOM.DIST.RANGE 327 BINOM.INV 328 329 330 331 CHISQ.DIST CHISQ.DIST.RT CHISQ.INV CHISQ.INV.RT 332 333 334 CHISQ.TEST CONFIDENCE.NORM CONFIDENCE.T 335 336 337 338 339 CORREL COUNT COUNTA COUNTBLANK COUNTIF 340 COUNTIFS 341 COVARIANCE.P 342 COVARIANCE.S 343 344 345 346 347 DEVSQ EXPON.DIST F.DIST F.DIST.RT F.INV www.CarltonCollins.com Returns the average of the absolute deviations of data points from their mean Returns the average of its arguments Returns the average of its arguments, including numbers, text, and logical values Returns the average (arithmetic mean) of all the cells in a range that meet a given criteria Returns the average (arithmetic mean) of all cells that meet multiple criteria Returns the beta cumulative distribution Returns the inverse of the cumulative distribution for a specified beta distribution Returns the individual term binomial distribution probability Returns the probability of a trial result using a binomial distribution Returns the smallest value for which the cumulative binomial distribution is less than or equal to a criterion value Returns the cumulative beta probability density Returns the one-tailed probability of the chi-squared distribution Returns the cumulative beta probability density Returns the inverse of the one-tailed probability of the chi-squared distribution Returns the test for independence Returns the confidence interval for a population mean Returns the confidence interval for a population mean, using a Student's t distribution Returns the correlation coefficient between two data sets Counts how many numbers are in the list of arguments Counts how many values are in the list of arguments Counts the number of blank cells within a range Counts the number of cells within a range that meet the given criteria Counts the number of cells within a range that meet multiple criteria Returns covariance, the average of the products of paired deviations Returns the sample covariance, the average of the products deviations for each data point pair in two data sets Returns the sum of squares of deviations Returns the exponential distribution Returns the F probability distribution Returns the F probability distribution Returns the inverse of the F probability distribution Page 133 Copyright April 2013 Using Excel to Detect Fraud 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 F.INV.RT F.TEST FISHER FISHERINV FORECAST FREQUENCY GAMMA GAMMA.DIST GAMMA.INV GAMMALN GAMMALN.PRECISE GAUSS GEOMEAN GROWTH HARMEAN HYPGEOM.DIST INTERCEPT KURT LARGE LINEST LOGEST LOGNORM.DIST LOGNORM.INV MAX MAXA 373 374 375 MEDIAN MIN MINA 376 MODE.MULT 377 378 379 380 381 382 383 384 MODE.SNGL NEGBINOM.DIST NORM.DIST NORM.INV NORM.S.DIST NORM.S.INV PEARSON PERCENTILE.EXC www.CarltonCollins.com Returns the inverse of the F probability distribution Returns the result of an F-test Returns the Fisher transformation Returns the inverse of the Fisher transformation Returns a value along a linear trend Returns a frequency distribution as a vertical array Returns the Gamma value Returns the gamma distribution Returns the inverse of the gamma cumulative distribution Returns the natural logarithm of the gamma, Γ(x) Returns the natural logarithm of the gamma, Γ(x) Returns 0.5 less than the standard normal cumulative distribution Returns the geometric mean Returns values along an exponential trend Returns the harmonic mean Returns the hypergeometric distribution Returns the intercept of the linear regression line Returns the kurtosis of a data set Returns the k-th largest value in a data set Returns the parameters of a linear trend Returns the parameters of an exponential trend Returns the cumulative lognormal distribution Returns the inverse of the lognormal cumulative distribution Returns the maximum value in a list of arguments Returns the maximum value in a list of arguments, including numbers, text, and logical values Returns the median of the given numbers Returns the minimum value in a list of arguments Returns the smallest value in a list of arguments, including numbers, text, and logical values Returns a vertical array of the most frequently occurring, or repetitive values in an array or range of data Returns the most common value in a data set Returns the negative binomial distribution Returns the normal cumulative distribution Returns the inverse of the normal cumulative distribution Returns the standard normal cumulative distribution Returns the inverse of the standard normal cumulative distribution Returns the Pearson product moment correlation coefficient Returns the k-th percentile of values in a range, where k is in the range 0..1, exclusive Page 134 Copyright April 2013 Using Excel to Detect Fraud 385 386 PERCENTILE.INC PERCENTRANK.EXC 387 388 389 PERCENTRANK.INC PERMUT PERMUTATIONA 390 391 392 PHI POISSON.DIST PROB 393 QUARTILE.EXC 394 395 396 397 QUARTILE.INC RANK.AVG RANK.EQ RSQ 398 399 SKEW SKEW.P 400 401 402 403 404 405 SLOPE SMALL STANDARDIZE STDEV.P STDEV.S STDEVA 406 STDEVPA 407 STEYX 408 T.DIST 409 T.DIST.2T 410 411 T.DIST.RT T.INV 412 413 414 T.INV.2T T.TEST TREND www.CarltonCollins.com Returns the k-th percentile of values in a range Returns the rank of a value in a data set as a percentage (0..1, exclusive) of the data set Returns the percentage rank of a value in a data set Returns the number of permutations for a given number of objects Returns the number of permutations for a given number of objects (with repetitions) that can be selected from the total objects Returns the value of the density for a standard normal distribution Returns the Poisson distribution Returns the probability that values in a range are between two limits Returns the quartile of the data set, based on percentile values from 0..1, exclusive Returns the quartile of a data set Returns the rank of a number in a list of numbers Returns the rank of a number in a list of numbers Returns the square of the Pearson product moment correlation coefficient Returns the skewness of a distribution Returns the skewness of a distribution based on a population: a characterization of the degree of asymmetry of a distribution around its mean Returns the slope of the linear regression line Returns the k-th smallest value in a data set Returns a normalized value Calculates standard deviation based on the entire population Estimates standard deviation based on a sample Estimates standard deviation based on a sample, including numbers, text, and logical values Calculates standard deviation based on the entire population, including numbers, text, and logical values Returns the standard error of the predicted y-value for each x in the regression Returns the Percentage Points (probability) for the Student tdistribution Returns the Percentage Points (probability) for the Student tdistribution Returns the Student's t-distribution Returns the t-value of the Student's t-distribution as a of the probability and the degrees of freedom Returns the inverse of the Student's t-distribution Returns the probability associated with a Student's t-test Returns values along a linear trend Page 135 Copyright April 2013 Using Excel to Detect Fraud 415 416 417 418 TRIMMEAN VAR.P VAR.S VARA 419 VARPA 420 421 WEIBULL.DIST Z.TEST Returns the mean of the interior of a data set Calculates variance based on the entire population Estimates variance based on a sample Estimates variance based on a sample, including numbers, text, and logical values Calculates variance based on the entire population, including numbers, text, and logical values Returns the Weibull distribution Returns the one-tailed probability-value of a z-test Text 422 ASC Changes full-width (double-byte) English letters or katakana within a character string to half-width (single-byte) characters 423 424 425 426 427 428 BAHTTEXT CHAR CLEAN CODE CONCATENATE DBCS Converts a number to text, using the ß (baht) currency format Returns the character specified by the code number Removes all nonprintable characters from text Returns a numeric code for the first character in a text string Joins several text items into one text item Changes half-width (single-byte) English letters or katakana within a character string to full-width (double-byte) characters 429 430 431 432 433 434 435 436 DOLLAR EXACT FIND, FINDBs FIXED LEFT, LEFTBs LEN, LENBs LOWER MID, MIDBs 437 438 439 440 441 442 443 444 445 446 447 NUMBERVALUE PHONETIC PROPER REPLACE, REPLACEBs REPT RIGHT, RIGHTBs SEARCH, SEARCHBs SUBSTITUTE T TEXT TRIM Converts a number to text, using the $ (dollar) currency format Checks to see if two text values are identical Finds one text value within another (case-sensitive) Formats a number as text with a fixed number of decimals Returns the leftmost characters from a text value Returns the number of characters in a text string Converts text to lowercase Returns a specific number of characters from a text string starting at the position you specify Converts text to number in a locale-independent manner Extracts the phonetic (furigana) characters from a text string Capitalizes the first letter in each word of a text value Replaces characters within text Repeats text a given number of times Returns the rightmost characters from a text value Finds one text value within another (not case-sensitive) Substitutes new text for old text in a text string Converts its arguments to text Formats a number and converts it to text Removes spaces from text www.CarltonCollins.com Page 136 Copyright April 2013 Using Excel to Detect Fraud 448 UNICHAR 449 UNICODE 450 451 UPPER VALUE Returns the Unicode character that is references by the given numeric value Returns the number (code point) that corresponds to the first character of the text Converts text to uppercase Converts a text argument to a number User defined that are installed with add-ins 452 453 CALL EUROCONVERT Calls a procedure in a dynamic link library or code resource Converts a number to euros, converts a number from euros to a euro member currency, or converts a number from one euro member currency to another by using the euro as an intermediary (triangulation) Returns the register ID of the specified dynamic link library (DLL) or code resource that has been previously registered 454 REGISTER.ID 455 SQL.REQUEST Connects with an external data source and runs a query from a worksheet, then returns the result as an array without the need for macro programming 453 454 ENCODEURL FILTERXML Returns a URL-encoded string Returns specific data from the XML content by using the specified XPath 455 WEBSERVICE Web www.CarltonCollins.com Page 137 Copyright April 2013 Using Excel to Detect Fraud The 171 Most Relevant and Important Functions to CPAs (According to Carlton’s best guess) The numbers in parenthesis correspond to the order in which each function appears on the All Functions worksheet Open an Excel file Containing Function Examples: http://www.ASAResearch.com/web/functions.xlsx The goal of this summary is to help CPAs focus on learning the most relevant and important functions first, without having to waste time wading through all 455 functions. Excel 2013 provides 455 functions, but in most cases only 37.5% of them are relevant and important to CPAs. Consider, how often do you expect to use the CRITBINOMIALDIST function? How often will the IMAGINARY function have relevance to your work? When did any CPA ever calculate depreciation using the SYD method (other than on a CPA example question). The reality is that CPAs don’t have to know all of the functions to master Excel, you’ll conquer Excel just by learning 37.5% of the included functions. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. IF (213) - Specifies a logical test to perform. SUM (307) - Adds its arguments. SUBTOTAL (306) - Returns a subtotal in a list or database. SUMIF (308) - Adds the cells specified by a given criteria. COUNT (336) - Counts how many numbers are in the list of arguments. COUNTA (337) - Counts how many values are in the list of arguments. AVERAGE (319) - Returns the average of its arguments. COUNTBLANK (338) - Counts the number of blank cells within a range. COUNTIF (339) - Counts the number of cells within a range that meet the given criteria. VALUE (451) - Converts a text argument to a number. TEXT (446) - Formats a number and converts it to text. VLOOKUP (238) - Looks in the first column of an array and moves across the row to return the value of the indicated cell. HLOOKUP (227) - Looks in the top row of an array and returns the value of the indicated cell. TWO WAY LOOKUP – Using both VLOOKUP and HLOOKUP together. LOOKUP (231) - Looks up values in a vector or array. MATCH (232) - Looks up values in a reference or array. TRIM (447) - Removes spaces from text. PROPER (439) - Capitalizes the first letter in each word of a text value. LOWER (435) - Converts text to lowercase. UPPER (450) - Converts text to uppercase. www.CarltonCollins.com Page 138 Copyright April 2013 Using Excel to Detect Fraud 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. LEFT, LEFTBs (433) - Returns the leftmost characters from a text value. RIGHT, RIGHTBs (442) - Returns the rightmost characters from a text value. MID, MIDBs (436) - Returns a specific number of characters from a text string starting at the position. FIND, FINDBs (431) - Finds one text value within another (case-sensitive). SUBSTITUTE (444) - Substitutes new text for old text in a text string. LEN, LENBs (434) - Returns the number of characters in a text string. REPLACE, REPLACEBs (440) - Replaces characters within text. CONCATENATE (427) - Joins several text items into one text item. CLEAN (425) - Removes all nonprintable characters from text. NOW (71) - Returns the serial number of the current date and time. TODAY (75) - Returns the serial number of today's date. DATE (58) - Returns the serial number of a particular date. MONTH (68) - Converts a serial number to a month. DAY (60) - Converts a serial number to a day of the month. YEAR (80) - Converts a serial number to a year. WEEKDAY (76) - Converts a serial number to a day of the week. ROUND (295) - Rounds a number to a specified number of digits. ROUNDDOWN (296) - Rounds a number down, toward zero. ROUNDUP (297) - Rounds a number up, away from zero. MAX (371) - Returns the maximum value in a list of arguments. DMAX (50) - Returns the maximum value from selected database entries. MIN (374) - Returns the minimum value in a list of arguments. DMIN (51) - Returns the minimum value from selected database entries. MEDIAN - Returns the median of the given numbers. MODE - Returns the most common value in a data set. PERCENTILE (25) - Returns the k-th percentile of values in a range. PERCENTRANK (26) - Returns the percentage rank of a value in a data set. PMT (171) - Returns the periodic payment for an annuity. NPV (165) - Returns the net present value of an investment based on a series of periodic cash flows. DSUM (55) - Adds the numbers in the field column of records in the database that match the criteria. DCOUNT (47) - Counts the cells that contain numbers in a database. DCOUNTA (48) - Counts nonblank cells in a database. AND (211) - Returns TRUE if all of its arguments are TRUE. OR (217) - Returns TRUE if any argument is TRUE. CHOOSE (222) - Chooses a value from a list of values. www.CarltonCollins.com Page 139 Copyright April 2013 Using Excel to Detect Fraud 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83. 84. 85. 86. 87. 88. TIME (73) - Returns the serial number of a particular time. FV (155) - Returns the future value of an investment. IRR (159) - Returns the internal rate of return for a series of cash flows. YIELD (188) - Returns the yield on a security that pays periodic interest. CELL (191) - Returns information about the formatting, location, or contents of a cell. Note INFO (193) - Returns information about the current operating environment. Note This is not avail. ERROR.TYPE (192) - Returns a number corresponding to an error type. ISBLANK (194) - Returns TRUE if the value is blank. ISNA (200) - Returns TRUE if the value is the #N/A error value. GETPIVOTDATA (226) - Returns data stored in a PivotTable report. HYPERLINK (228) - Creates a shortcut or jump that opens a document stored on a network server, an in. TRANSPOSE (237) - Returns the transpose of an array. ABS (239) - Returns the absolute value of a number. RAND (292) - Returns a random number between 0 and 1. RANDBETWEEN (293) - Returns a random number between the numbers you specify. REPT (441) - Repeats text a given number of times. SLN (180) - Returns the straight-line depreciation of an asset for one period. SYD - Returns the sum-of-years' digits depreciation of an asset for a specified period. DDB (149) - Returns the depreciation of an asset for a specified period by using the double-declining balance method. DGET (49) - Extracts from a database a single record that matches the specified criteria. ADDRESS (220) - Returns a reference as text to a single cell in a worksheet. AGGREGATE (244) - Returns an aggregate in a list or database. FORECAST (352) - Returns a value along a linear trend. GROWTH (361) - Returns values along an exponential trend. LARGE (366) - Returns the k-th largest value in a data set. NOT (216) - Reverses the logic of its argument. OFFSET (233) - Returns a reference offset from a given reference. PEARSON (383) - Returns the Pearson product moment correlation coefficient. PV (176) - Returns the present value of an investment. RATE (177) - Returns the interest rate per period of an annuity. SEARCH, SEARCHBs (443) - Finds one text value within another (not case-sensitive). SMALL (401) - Returns the k-th smallest value in a data set. XIRR (186) - Returns the internal rate of return for a schedule of cash flows that is not necessarily. www.CarltonCollins.com Page 140 Copyright April 2013 Using Excel to Detect Fraud 89. 90. 91. 92. 93. 94. 95. 96. 97. 98. 99. 100. 101. 102. 103. 104. 105. 106. 107. 108. 109. 110. 111. 112. 113. 114. 115. 116. 117. 118. 119. 120. XOR (219) - Returns a logical exclusive OR of all arguments. AVERAGEIF (321) - Returns the average (arithmetic mean) of all the cells in a range that meet a give. AVERAGEIFS (322) - Returns the average (arithmetic mean) of all cells that meet multiple criteria. COLUMN (223) - Returns the column number of a reference. COLUMNS (224) - Returns the number of columns in a reference. CONVERT (95) - Converts a number from one measurement system to another. COUNTIFS (340) - Counts the number of cells within a range that meet multiple criteria. DATEVALUE (59) - Converts a date in the form of text to a serial number. DECIMAL (263) - Converts a text representation of a number in a given base into a decimal number. DOLLAR (429) - Converts a number to text, using the $ (dollar) currency format. EXACT (430) - Checks to see if two text values are identical. FORMULATEXT (225) - Returns the formula at the given reference as text. HOUR (65) - Converts a serial number to an hour. INDEX (229) - Uses an index to choose a value from a reference or array. ISERR (195) - Returns TRUE if the value is any error value except #N/A. ISERROR (196) - Returns TRUE if the value is any error value. ISFORMULA (198) - Returns TRUE if there is a reference to a cell that contains a formula. KURT (365) - Returns the kurtosis of a data set. MINUTE (67) - Converts a serial number to a minute. NA (207) - Returns the error value #N/A. PPMT (172) - Returns the payment on the principal for an investment for a given period. ROW (234) - Returns the row number of a reference. ROWS (235) - Returns the number of rows in a reference. RSQ (397) - Returns the square of the Pearson product moment correlation coefficient. SHEET (208) - Returns the sheet number of the referenced sheet. SHEETS (209) - Returns the number of sheets in a reference. SKEW (398) - Returns the skewness of a distribution. SKEW.P (399) - Returns the skewness of a distribution based on a population: a characterization of t. SUMIFS (309) - Adds the cells in a range that meet multiple criteria. T (445) - Converts its arguments to text. TYPE (210) - Returns a number indicating the data type of a value. WEEKNUM (77) - Converts a serial number to a number representing where the week falls numerically with a year. www.CarltonCollins.com Page 141 Copyright April 2013 Using Excel to Detect Fraud 121. 122. 123. 124. 125. 126. 127. 128. 129. 130. 131. 132. 133. 134. 135. 136. 137. 138. 139. 140. 141. 142. 143. 144. 145. 146. 147. 148. 149. WORKDAY (78) - Returns the serial number of the date before or after a specified number of workdays. XNPV (187) - Returns the net present value for a schedule of cash flows that is not necessarily periodic. EFFECT (154) - Returns the effective annual interest rate. INT (273) - Rounds a number down to the nearest integer. INTERCEPT (364) - Returns the intercept of the linear regression line. RANK (29) - Returns the rank of a number in a list of numbers. RRI (179) - Returns an equivalent interest rate for the growth of an investment. SIGN (301) - Returns the sign of a number. SLOPE (400) - Returns the slope of the linear regression line. CEILING (252) - Rounds a number to the nearest integer or to the nearest multiple of significance. CEILING.MATH (253) - Rounds a number up, to the nearest integer or to the nearest multiple of significance. CHAR (424) - Returns the character specified by the code number. CODE (426) - Returns a numeric code for the first character in a text string. DAVERAGE (46) - Returns the average of selected database entries. DAYS (61) - Returns the number of days between two dates. FALSE (212) - Returns the logical value FALSE. FIXED (432) - Formats a number as text with a fixed number of decimals. FLOOR.MATH (270) - Rounds a number down, to the nearest integer or to the nearest multiple of significance . IFERROR (214) - Returns a value you specify if a formula evaluates to an error; otherwise, returns t. IFNA (215) - Returns the value you specify if the expression resolves to #N/A, otherwise returns the. INDIRECT (230) - Returns a reference indicated by a text value. IPMT (158) - Returns the interest payment for an investment for a given period. ISNONTEXT (201) - Returns TRUE if the value is not text. ISNUMBER (202) - Returns TRUE if the value is a number. MAXA (372) - Returns the maximum value in a list of arguments, including numbers, text, and logical . MINA (375) - Returns the smallest value in a list of arguments, including numbers, text, and logical. N (206) - Returns a value converted to a number. NETWORKDAYS (69) - Returns the number of whole workdays between two dates. RECEIVED (178) - Returns the amount received at maturity for a fully invested security. www.CarltonCollins.com Page 142 Copyright April 2013 Using Excel to Detect Fraud 150. 151. 152. 153. 154. 155. 156. 157. 158. 159. 160. 161. 162. 163. 164. 165. 166. 167. 168. 169. 170. 171. 172. SECOND (72) - Converts a serial number to a second. SUMPRODUCT (310) - Returns the sum of the products of corresponding array components. TREND (414) - Returns values along a linear trend. TRUE (218) - Returns the logical value TRUE. ACCRINT (136) - Returns the accrued interest for a security that pays periodic interest. ACCRINTM (137) - Returns the accrued interest for a security that pays interest at maturity. ISPMT (160) - Calculates the interest paid during a specific period of an investment. ISTEXT (205) - Returns TRUE if the value is text. LINEST (367) - Returns the parameters of a linear trend. CUBEKPIMEMBER (39) - Returns a key performance indicator (KPI) property and displays the KPI name in. DELTA (99) - Tests whether two values are equal. EVEN (265) - Rounds a number up to the nearest even integer. ISEVEN (197) - Returns TRUE if the number is even. ISODD (203) - Returns TRUE if the number is odd. ISOWEEKNUM (66) - Returns the number of the ISO week number of the year for a given date. LOGEST (368) - Returns the parameters of an exponential trend. ODD (286) - Rounds a number up to the nearest odd integer. PRODUCT (289) - Multiplies its arguments. QUOTIENT (290) - Returns the integer portion of a division. STDEVP (31) - Calculates standard deviation based on the entire population. TBILLEQ (182) - Returns the bond-equivalent yield for a Treasury bill. TBILLYIELD (184) - Returns the yield for a Treasury bill. TIMEVALUE (74) - Converts a time in the form of text to a serial number. www.CarltonCollins.com Page 143 Copyright April 2013 Using Excel to Detect Fraud Bio for J. Carlton Collins, CPA ASA Research Carlton@ASAResearch.com 770.842.5902 J. Carlton Collins, CPA is a Certified Public Accountant with experience in technology, tax, auditing, accounting systems, financial reporting, and bond financing. He is an author, lecturer, and technology & accounting systems consultant. He has published books, articles, and web pages and is the author of the monthly technology Q&A column for the Journal of Accountancy. As a public speaker, Mr. Collins has delivered more than 2,000 lectures in 44 states and 5 countries addressing more than 500,000 CPAs and business professionals. As a consultant, Mr. Collins has assisted 275+ large and small companies with the selection and implementation of accounting systems. Mr. Collins has a Bachelor’s degree in Accounting from the University of Georgia, is a 25+ year member of the American Institute of CPAs and the Georgia Society of CPAs. Summary of Selected Positions, Awards & Accomplishments: 1. Honored as one of the CPA Industries Top 25 Thought Leaders by CPA Technology Advisor Magazine 2. Author of the monthly Technology Q&A column for the Journal of Accountancy. 3. Recipient of the AICPA’s Lifetime Technical Contribution to the CPA Profession Award. 4. Chairman of the Southeast Accounting Show - the South’s largest CPA event. 5. Recipient of the Tom Radcliff Outstanding Discussion Leader Award. 6. Named “Top Ten CPA Technologists” by Accounting Technologies Magazine (multiple years). 7. Named “Top 100 Most Influential CPAs ” by Accounting Technologies Magazine (multiple years). 8. Has personally delivered over 2,000 technology lectures around the world. 9. Recipient of the Outstanding Discussion Leader Award from the Georgia Society of CPAs. 10. Lead author for PPC's Guide to Installing Microcomputer Accounting Systems. 11. Has installed accounting systems for more than 200 companies. 12. Chairperson of the AICPA Technology Conference. 13. Recipient of the ACCPAC Partner of the Year Award. 14. Determined by SAP to be one of the country's "Top Ten Most Influential ERP Systems Consultants". 15. Has delivered keynote and session lectures at dozens of accounting software conferences. 16. Sworn in as a Certified Public Accountant on September 18, 1985. 17. Member of the American Institute of CPAs since 1985. 18. Member of the Georgia Society of CPAs since 1982. As an auditor, Mr. Collins has audited businesses in the areas of health care, construction, distribution, automobile dealerships, insurance, manufacturing, and general business. Mr. Collins' tax experience includes corporate, individual, partnership, fiduciary, and estate tax planning work. In the area of finance, Mr. Collins has prepared (or assisted in preparing) feasibility studies and financial forecasts for nearly 300 projects seeking more than $3 billion in startup capital. Mr. Collins is familiar with bond issues, Medicare and Medicaid reimbursement, and conventional financing matters. In 1992, Mr. Collins contributed and demonstrated more than 500 pages of suggested design improvements to the Microsoft Excel development team of programmers - and many of those improvements are found in Excel today. At the University of Georgia, Mr. Collins was elected President of the Phi Eta Sigma Honor Society, was initiated into the BIFTAD Honor Society, served three years in the Judicial Defender/Advocate program, and was a member of Alpha Tau Omega fraternity. At Glynn Academy High School, Mr. Collins was Senior Class President, Class Valedictorian (1 of 6), and received a principle nomination to Annapolis Naval Academy. Mr. Collins has been married for 27 years and has two children. He devotes his leisure time to family, travel, tennis, fishing, snow skiing, and riding motorcycles (both dirt and street). Mr. Collins is president of his homeowners association, participates in the Gwinnett Clean and Beautiful program, and volunteers for Cooperative Ministries food drive. www.CarltonCollins.com Page 144 Copyright April 2013

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