Using Data Analytics for Fraud Detection

1. Using Data Analytics for Fraud Detection Amanda J. Bernard, CPA, CFE, CMA Principal, Maillie LLP abernard@maillie.com

2. Agenda Topic 1: Definition and Benefits Topic 2: Pivot Tables and Summarization Topic 3: Benford’s Law Analysis Topic 4: Matching and Duplicates Topic 5: Word Clouds

3. Using Data Analytics for Fraud Detection What is data analytics? • Process of examining data sets in order to draw conclusions about the information they contain Trends toward data analytics • More data • Availability of data • Advanced technology • Advances in visualization tools

4. Using Data Analytics for Fraud Detection Audit analytics • “The science and art of discovering and analyzing patterns, identifying anomalies, and extracting other useful information in data underlying or related to the subject matter of an audit through analysis, modeling, and visualization for the purpose of planning or performing the audit.” –AICPA Guide to Audit Data Analytics, December 2017 Fraud analytics • Combination of analytic technology and fraud analysis techniques with human interaction used to detect improper transactions.

5. Using Data Analytics for Fraud Detection Benefits of Data Analytics • Timely identification of fraud or control problems • Test 100% of data (no sampling) • Compare data across multiple systems • Maintain comprehensive log of investigation activities • Automation for easy repetition of test steps

6. Using Data Analytics for Fraud Detection • Who is currently using data analytics? • Life sciences • Banking • Manufacturing • Health care and insurance • Government • Retail • Farming and agriculture • Internal audit • Legal and compliance • Operational management • Purchasing • Inventory control • Sales and marketing • IT • HR

7. Using Data Analytics for Fraud Detection • Predictions for the future • Volume of data will continue to grow • New analysis tools and methods will emerge • Software speed and abilities will improve • Prescriptive, built-in analytics • Ongoing monitoring and continuous auditing • AI and machine learning

8. Using Data Analytics for Fraud Detection • Predictions for the future • Privacy concerns • Data ownership • Speed will allow for real-time decision making • All companies will become data driven • High demand for individuals with data mining and analysis skill sets • Data-as-a-service business model

9. Using Data Analytics for Fraud Detection • How are auditors and accountants using data analytics? • Matching and joining reports from different platforms • Visualizing trends • Risk assessment • Substantive audit testing • Journal entry testing • Fraud detection

10. Using Data Analytics for Fraud Detection • Fraud prevention • Control the “opportunity” portion of the fraud triangle An external incentive or pressure exists on employees to misappropriate cash or other organizational assets A frame of mind exists that allows employees to misappropriate cash or other organizational assets and justify their dishonest actions Circumstances enable an employee to carry out the misappropriation of cash or other organizational assets

11. Using Data Analytics for Fraud Detection Why apply fraud analytics? • Internal controls are not sufficient on their own • Reactive and detective measures • Predictive and preventative measures What are we looking for? • Patterns, trends and red flags • Specific scenarios under which fraud routinely takes place

12. Using Data Analytics for Fraud Detection • What will the analytic be used for? • What data will be required? Is the data reliable? • Which analytics are most appropriate?

13. Using Data Analytics for Fraud Detection • Pinpoint areas for investigation using “what could go wrong and how could it happen?” brainstorming before you start • If an employee wanted to pay themselves through the vendor • system, how could they do it? • If purchasing wanted to avoid management approval, how could • they do it? • If someone wanted to create a fictitious employee, how could • they do it? • If management wanted to misstate the financials, how could they do it?

14. Pivot Tables and Summarization

15. Pivot Tables and Summarization • Uses • Analyze disaggregated data • Look for trends over time • Review totals by department, location, or category • Compare totals by vendor, customer, or employee

16. Pivot Tables and Summarization Pivot table of expenses over time

17. Pivot Tables and Summarization Total paid by vendor

18. Pivot Tables and Summarization Number/frequency of payments processed by vendor for similar items

19. Benford’s Law Analysis

20. Benford’s Law Analysis • Benford’s Law is defined as: • The principle that in any large, randomly produced set of natural numbers, such as tables of logarithms or corporate sales statistics, around 30 percent will begin with the digit 1, 18 percent with 2, and so on, with the smallest percentage beginning with 9. • Source: Oxford Dictionary

21. Benford’s Law Analysis • Data that conforms to Benford’s Law should: • Measure the size or values of events • Have no artificial minimum or maximum value (other than less than zero) • Not be labels or identification numbers • Be made up of more small numbers than large numbers • Not cluster around an average value • Analyze expense and revenue data for conformity with Benford’s Law

22. Benford’s Law Analysis • Analytical method: • Import data into data analysis software • Software will analyze the data and determine the level of • conformity with Benford’s Law • Detailed analysis of non-conforming data • Often reasonable explanation for non-conforming data • Testing is performed on both the first digit and the first two • digits.

23. Benford’s Law Analysis • First digit test with close conformity

24. Benford’s Law Analysis • First two digits test with acceptable conformity

25. Benford’s Law Analysis • First digit test with non-conformity

26. Benford’s Law Analysis • First two digits test with non-conformity

27. Benford’s Law Analysis • Examples of non-conforming data: • Fees collected for a flat amount • Several employees with the same payroll • Allocation of expenses to different departments • Recurring employee reimbursements

28. Matching and Duplicates

29. Matching and Duplicates • Identifies links between individuals that could • indicate potential corruption, collusion or conflicts of interest • Locates potential errors or control deficiencies • Duplicates = matches within the same file

30. Matching and Duplicates • Uses/Tests: • Address, telephone number, e-mail address and • bank account matches • Duplicate employee SSNs • Comparison to master files • Duplicate vendor invoice payments • Data cleansing

31. Matching and Duplicates • Address match - • Employees • Customers • Vendors

32. Matching and Duplicates • Duplicate vendor invoice payments • Vendor number/name • Amount

33. Matching and Duplicates • Duplicate vendor invoice payments • Vendor number/name • Amount

34. Matching and Duplicates • Other factors to consider • Relationships do not always = fraud • Imperfect data • Typos • Variations in spelling and abbreviation • Use of overrides such as “99999” for zipcode

35. Matching and Duplicates • The problem of imperfect data • Solution - probability record linkage, “fuzzy matching”

36. Matching and Duplicates • The problem of imperfect data • Solution - probability record linkage, “fuzzy matching”

37. Word Clouds

38. Word Clouds • Word Cloud: • An image composed of words used in a particular test in • which the size of each word indicates its frequency or • importance. • Benefits: • Analyze words in addition to numbers • Get a feel for the data before analyzing • Certain words can represent red flags for fraud or other • trouble within business operations

39. Word Clouds

40. Word Clouds • Methods to create Word Clouds • Created via the internet • Address: https://worditout.com/word-cloud/create • Benefits • Ease of use • No software to install and configure • Drawbacks • Transmitting data over the internet • Need to cleanse data for any identifiable information

41. Word Clouds Methods to create Word Clouds • Microsoft Word using Pro Word Cloud Add-in • Benefits • No need to transmit data over the internet • More customizable word cloud document • Drawbacks • Can be difficult to configure • Need to install additional add-ins in Microsoft Word

42. Word Clouds • Use in Fraud Testing • Create a word cloud using the description field of the • general ledger • The more frequently the word appears in the data the larger • its representation in the word cloud • Review larger words for indicators of fraud (adjustment, • void, reconcile, etc.) • Analyze entries that contain these words and determine if • there are any issues

43. Word Clouds • Other Uses of Word Clouds • Review of e-mail text for possible employee discontent • Determination of most utilized vendors • Single audit allowable activities

44. Word Clouds Positive Negative

45. Word Clouds Can you guess the classic novel?

46. Word Clouds Can you guess the classic novel?

47. Word Clouds Can you guess the classic novel?

48. _____________________________________________________ • Thank You • Amanda Bernard, CPA, CFE, CMA • abernard@maillie.com