Detection of Fraud

1. Detection ofFraud Digital Analysis Patterns Using May 16, 2008

2. Session objectives • Understand why and how • Understand statistical basis for quantifying differences • Identify ten general tools and techniques • Understand use of Excel • How pattern detection fits in

3. Overview • Fraud patterns detectable with digital analysis • Basis for digital analysis approach • Usage examples • Continuous monitoring • Business analytics • Using Excel

4. Objective 1 The Why and How • Three brief examples • IIA Guidance Paper • Auditors “Top 10” • Process Overview • Who, What, Why, When & Where

5. Objective 1a Example 1Wake County Transportation Fraud • Supplier Kickback – School Bus parts • $5 million • Jail sentences • Period of years

6. Objective 1a Too little too late • Understaffed internal audit • Software not used • Data on multiple platforms • Transaction volumes large

7. Objective 1a Preventable • Need structured, objective approach • Let the data “talk to you” • Need efficient and effective approach

8. Example 2 Objective 1a Cost of six types of AIDS drugs

9. Objective 1a Example 2 Typical Prescription Patterns

10. Example 2 Objective 1a Prescriptions by Dr. X

11. Example 2 Objective 1a Off-label use • Serostim • Treat wasting syndrome, side effect of AIDS, OR • Used by body builders for recreational purposes • One physician prescribed $11.5 million worth (12% of the entire state)

12. Example 3 Objective 1a Revenue trends

13. Example 3 Objective 1a Dental Billings

14. Objective 1b Guidance Paper • A proposed implementation approach • “Managing the Business Risk of Fraud: A Practical Guide” http://tinyurl.com/3ldfza • Five Principles • Fraud Detection • Coordinated Investigation Approach

15. Objective 1b Managing the Business Risk of Fraud: A Practical Guide • Exposure draft of IIA, AICPA and ACFE • Exposure draft issued 11/2007 • Section 5 – Fraud Detection

16. Objective 1b Section 5 – Fraud Detection • Detective Controls • Process Controls • Anonymous Reporting • Internal Auditing • Proactive Fraud Detection

17. Objective 1b Proactive Fraud Detection • Data Analysis to identify: • Anomalies • Trends • Risk indicators

18. Objective 1b Specific Examples Cited • Journal entries – suspicious transactions • Identification of relationships • Benford’s Law • Continuous monitoring

19. Objective 1b Data Analysis enhances ability to detect fraud • Identify hidden relationships • Identify suspicious transactions • Assess effectiveness of internal controls • Monitor fraud threats • Analyze millions of transactions

20. Objective 1c Peeling the Onion

21. Objective 1d Fraud Pattern Detection

22. Objective 1e Digital Analysis (5W) • Who • What • Why • Where • When

23. Objective 1e Who Uses Digital Analysis • Traditionally, IT specialists • With appropriate tools, audit generalists (CAATs) • Growing trend of business analytics • Essential component of continuous monitoring

24. Objective 1e What - Digital Analysis • Using software to: • Classify • Quantify • Compare • Both numeric and non-numeric data

25. Objective 1e How - Assessing fraud risk • Basis is quantification • Software can do the “leg work” • Statistical measures of difference • Chi square • Kolmogorov-Smirnov • D-statistic • Specific approaches

26. Objective 1e Why - Advantages • Automated process • Handle large data populations • Objective, quantifiable metrics • Can be part of continuous monitoring • Can produce useful business analytics • 100% testing is possible • Quantify risk • Repeatable process

27. Objective 1e Why - Disadvantages • Costly (time and software costs) • Learning curve • Requires specialized knowledge

28. Objective 1e When to Use Digital Analysis • Traditional – intermittent (one off) • Trend is to use it as often as possible • Continuous monitoring • Scheduled processing

29. Objective 1e Where Is It Applicable? • Any organization with data in digital format, and especially if: • Volumes are large • Data structures are complex • Potential for fraud exists

30. Objective 1 Objective 1 Summarized • Three brief examples • IIA Guidance Paper • “Top 10” Metrics • Process Overview • Who, What, Why, When & Where

31. Objective 1 - Summarized • Understand why and how • Understand statistical basis for quantifying differences • Identify ten general tools and techniques • Understand use of Excel • How pattern detection fits in Next is the basis …

32. Objective 2 Basis for Pattern Detection • Analytical review • Isolate the “significant few” • Detection of errors • Quantified approach

33. Objective 2 Understanding the Basis • Quantified Approach • Population vs. Groups • Measuring the Difference • Stat 101 – Counts, Totals, Chi Square and K-S • The metrics used

34. Objective 2a Quantified Approach • Based on measureable differences • Population vs. Group • “Shotgun” technique

35. Objective 2a Detection of Fraud Characteristics • Something is different than expected

36. Objective 2b Fraud patterns • Common theme – “something is different” • Groups • Group pattern is different than overall population

37. Objective 2c Measurement Basis • Transaction counts • Transaction amounts

38. Objective 2d A few words about statistics • Detailed knowledge of statistics not necessary • Software packages do the “number-crunching” • Statistics used only to highlight potential errors/frauds • Not used for quantification

39. Objective 2d How is digital analysis done? • Comparison of group with population as a whole • Can be based on either counts or amounts • Difference is measured • Groups can then be ranked using a selected measure • High difference = possible error/fraud

40. Objective 2d Histograms • Attributes tallied and categorized into “bins” • Counts or sums of amounts

41. Objective 2d Two histograms obtained • Population and group

42. Objective 2d Compute Cumulative Amount for each

43. Objective 2d Are the histograms different? • Two statistical measures of difference • Chi Squared (counts) • K-S (distribution) • Both yield a difference metric

44. Objective 2d Chi Squared • Classic test on data in a table • Answers the question – are the rows/columns different • Some limitations on when it can be applied

45. Objective 2d Chi Squared • Table of Counts • Degrees of Freedom • Chi Squared Value • P-statistic • Computationally intensive

46. Objective 2d Kolmogorov-Smirnov • Two Russian mathematicians • Comparison of distributions • Metric is the “d-statistic”

47. Objective 2d How is K-S test done? • Four step process • For each cluster element determine percentage • Then calculate cumulative percentage • Compare the differences in cumulative percentages • Identify the largest difference

48. Objective 2d - KS Kolmogorov-Smirnov

49. Objective 2e Classification by metrics • Stratification • Day of week • Happens on holiday • Round numbers • Variability • Benford’s Law • Trend lines • Relationships (market basket) • Gaps • Duplicates

50. Objective e Auditor’s “Top 10” Metrics • Outliers / Variability • Stratification • Day of Week • Round Numbers • Made Up Numbers • Market basket • Trends • Gaps • Duplicates • Dates