Detection using the XL Audit Commander

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Fraud Pattern detectionXL Audit CommanderDetection of Fraud PatternsOverviewThe purpose of the fraud pattern detection routines is to divide the population intocomponents and then quantitatively assess to what extent each component differs fromthe population as a whole, using one of seven types of attributes or tests:1. Existence of round numbers2. Benford’s Law3. Days of the week4. Occurrence of U.S. Federal holidays5. Coefficient of variation6. Data Stratification7. Trend lines (“best fit”)By automating the process of identification of population components which differsignificantly from the remainder of the population, the process is speeded up and thelikelihood of identifying error/fraud transactions is increased, making the auditor’s workmore efficient and effective.The approach taken is as follows for each type of test performed:Using the test specified, determine the attributes of the population as a wholeUsing the sort sequence specified, separate the population into unique groupsusing the sort parameters, i.e. each group extracted will consist of elements whichmatch using the sort parameters specified.For each group, perform the same test as that of the population as a whole. Thencompare the attributes of the selected group with that of the population as a whole.Differences can be quantified using one of three measures (which are explained in moredetail below):1. The d-statistic for the counts2. The d-statistic for the amounts3. The chi squared value for the counts(Note that for some tests, there may be no “amount” which can be tested, in which caseonly the two “count” tests may be performed.)All of the tests are done using data organized as a histogram (described more fully below)http://www.ezrstats.com Detection of Fraud Patterns EZ-R Stats, LLC © 2007-2008
HistogramsXL Audit CommanderDetection of Fraud PatternsA histogram is a means to classify numeric data. Each value or observation in thepopulation is classified by its value and placed into a “bin”. A “bin” is either a singlevalue or a range between two values. If the observed value fits within this “bin”, then thecount is updated by one and a total value for the bin (if applicable) is updated for theobservation’s amount or value.A very simple example is provided below to illustrate the process. The populationconsists of 21 values, which were randomly generated values between 0 and 100. Thesevalues are then classified as falling into one of five bins (0-20, 20-40, 40-60, 60-80 and80-100). The population classification then consists of the following counts:Of the 21 values, we can see that four (4) fall in the first bucket, two (2) in the secondbucket, six (6) in the third bucket, etc. This information can then be charted as follows:http://www.ezrstats.com Detection of Fraud Patterns EZ-R Stats, LLC © 2007-2008
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Detection using the XL Audit Commander

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