1. Jamie Ralls, CFE, ACDA Ian Green, M.Econ, CGAP Oregon Audits Division Association of Local Government Auditors December 9, 2015

2. Opening Remarks Moderator R. Kinney Poynter Executive Director NASACT Speaker Ian Green Senior Auditor Division of Audits (OR) 2 Speaker Jamie Ralls Principal Auditor Division of Audits (OR)

3. Polling Question #1 of 3 3

4. Agenda 1. History 2. What is Benford’s Law 3. Types of Data That Conform 4. Uses in Fraud Investigations 5. ACL and Excel Examples 6. Fraud analysis in the real world & other tests 7. Lessons learned 4

5. History Simon Newcomb – 1881 Frank Benford – 1938 Roger Pinkham – 1961 Mark Nigrini - 1989 5 Frank Benford - 1912 Logarithm Book

6. Polling Question #2 of 3 6

7. What is Benford’s Law It gives the probability of obtaining digits 1 through 9 in each position of a number Most people assume that the probability is 1/9 that the first digit will be 1-9 According to the law, the probability of obtaining a 1 in the first digit position is 30.1% For example 3879 3 – first digit 8 – second digit 7 – third digit 9 – fourth digit

8. 8 Enron Fraud

9. Benford’s Law Key Assumptions Benford’s is likely to work, if: The set of numbers is not limited All leading digits are possible (1,2,3,4,5,6,7,8,9) Numbers span multiple orders of magnitude For example, 1 to 10, 10 to 100, 100 to 1,000 Sample size is very large Use the entire population, if available Sample sizes under 100 won’t work Sample sizes under 1000 are not very reliable in most situations 9

10. Benford’s Law Overview cont. Mathematical Formula: P(d) = Log 10 (1+ 1/d) 10

11. Normal Distribution 11

12. What data works, what doesn’t Follows Benford’s Addresses Bank account balances Census data River drainage Population by country Most accounting related data, such as Accounts Receivable Transaction level data Doesn’t follow Benford’s Ages Heights Zip codes Payroll Invoice numbers Check numbers Product price ATM withdrawals 12 Note: Remember to check your dataset to see that it meets the required assumptions from slide 3, the above list does not work in all cases

13. Why… Bank account balances Numbers have no limit and span multiple orders of magnitude From $1 to $1 billion Available sample size of data is very large Nearly everyone has one ATM Withdrawals Upper and lower limit Minimum generally $20 Maximum generally $800 Numbers do not span multiple orders of magnitude Generally limited to multiples of $20 13

14. Choosing Appropriate Data Sets Works best on datasets with > 1,000 records The more records the better! Great for detecting weaknesses in internal controls with regards to purchase or reimbursement limits, that can be thwarted by submitting claims just below the limit 14

15. Benford’s Law Uses Payment Information – vendor payments, travel payments, and credit card payments. Example: State travel payments, excess leading digits of ’24’. Bad Debt Write offs – Example: excess leading digits of ‘49’, upon review they ended up finding a huge fraud where the bank rep was opening up credit cards for his friends and they were racking them up to just under 5,000, and then he was writing them off because the approval was $5,000 and over.

16. 16 Applying Fraud Analysis in the Real World

17. Other useful fraud detection tests Average transaction amount Percent of even dollar transactions Analyzing records of clients with a high number of “lost” cards High number of multiple same day/same time transactions This could indicate someone splitting fraudulent transactions to avoid detection Large distances traveled 17

18. Real World Example Food Stamp (SNAP) Fraud in Oregon 18 Store TypeAverage TransactionAverage % Even Convenience stores Mini markets 7-11 $6~5% Walmarts$33~5% Safeway/Albertsons$24~5% “Dollar” stores$847% Meat markets$3712% Carniceria Mi Pueblo$5451%

19. ACL Demo The following slides are screenshots of the ACL demo for your records. 19

20. Carniceria Mi Pueblo 20

21. Comparison Stores 21

22. 22

23. Benford’s on Second Digit 23

24. Second Digit Table 24

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26. Note how Carniceria’s percent of even dollar transactions steadily rose over the years, while comparison stores did not

27. 27 Note how Carniceria’s average transaction amount rose dramatically over the years, while comparison stores did not

28. Excel Demo The following slides are screenshots of the Excel demo for your records. 28

29. Excel 29

30. Excel 1 Digit 30

31. Excel 2 Digit 31

32. Distance Traveled Using ArcGIS 32

33. Polling Question #3 of 3 33

34. 34 Bank Balances – Follows Benfords Ages Office Addresses Heights Pre-Webinar Survey Results – Sample Size 158

35. 35 Home Addresses – Follows Benfords Drivers Licenses SSNs – 8 & 9 should not be possible unless issued after 2011 Favorite Numbers – Look at that bias! Pre-Webinar Survey Results – Sample Size 158

36. Lessons Learned Benford’s law will lead to false positives Business processes can change the distribution of leading digits to appear to violate Benford’s, when in fact, there is no fraud occurring Unusual patterns only indicate fraud Additional investigative work is required to prove fraud Benford’s will not detect all types of frauds For example, bribes and kickbacks are off the books and undetectable through data analysis 36

37. Putting it all together Work with law enforcement partners to deliver the information they need for their investigation and the trial Be ready for last minute rush requests Document your work as you go Prepare to testify before a jury 37

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39. Questions? Oregon Audits Division 255 Capitol Street NE STE 500 Salem, OR 97310 (503) 986-2255 Jamie.N.Ralls@state.or.us Ian.M.Green@state.or.us 39

40. Question and Answer Session Moderator R. Kinney Poynter Executive Director NASACT Speaker Ian Green Senior Auditor Division of Audits (OR) 40 Speaker Jamie Ralls Principal Auditor Division of Audits (OR)