1. © EZ-R Stats, LLC Duplicate Payments Slide 1 Auditing for Duplicate Payments A better way … Web CAAT

2. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 2 About duplicate payments Why they occur Fraud Errors Control breakdowns System Procedures How to detect

3. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 3 Historical Experience State of North Carolina Fiscal 1996 – 2004 $4.5 million recovered Approximately $500K / year Most recent experience About $400K/ year “Pay and Chase”

4. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 4 Matching approach Exact matching “Fuzzy” matching Every possible pair

5. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 5 Duplicate payments

6. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 6 Why check for them? Recovery fee Possibility of fraud Identify control break downs Proactive checking

7. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 7 Cost Recoveries “Pay and Chase” 35% fee to Cost Recovery Contractor Risk of loss Proactive Approach Identify up-front Make control recommendations to prevent Continuous monitoring

8. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 8 Invoice elements Vendor number Invoice number Invoice Date Invoice Amount

9. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 9 Exact matches All four elements match Three combinations of three way match Vendor, invoice, amount Vendor, invoice, date Vendor, amount, date

10. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 10 “Fuzzy” matches – invoice numbers Levenshtein distance Transpositions LDO (letters, digits only) Same characters Leading characters Trailing characters

11. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 11 Example of Levenshtein distance Measure similarity of invoice “12341” and “24371”. Start1234124371 Step1 – delete left most digit 234124371 Step2 – Insert a “4” between “2” and “3” 2434124371 Step 3- Replace seond “4” with “7” 24371 Levenshtein distance is 3

12. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 12 “Fuzzy” matches – invoice number

13. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 13 Date Transpositions 07/31/2010 vs. 07/13/2010 01/21/2009 vs. 02/11/2009

14. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 14 Data validation Invoice date Invoice amount Vendor number

15. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 15 Potential duplicates?

16. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 16 Potential duplicaes Menu item potential duplicates

17. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 17 Forms Browser based Pull down menus “Fill in the blanks”

18. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 18 Results Web based report Import into Excel

19. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 19 Processing volumes 500,000 invoices 40,000 vendors Process on lap-top with dual 2.2 GHz About two minutes per test

20. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 20 Road tested

21. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 21 Pricing

22. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 22 Demo Excel workbook 10,417 Payments 19 tests

23. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 23 Workbook Excel workbook, 10,417 payments, 10 columns

24. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 24 19 Tests can be run “A” – “S” Description of tests Used for identifying potential duplicate payments Same concept applies to other areas Journal entries Purchase orders Expense reports Fixed asset items, etc.

25. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 25 Test “A” All four key values equal Same vendor Same amount Same invoice date Same invoice number Note: case insensitive comparisons

26. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 26 Test “B” Same vendor, Same invoice number, Same invoice amount

27. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 27 Test “C” Same vendor number, Same invoice number, Same invoice date

28. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 28 Test “D” Same vendor, Same invoice amount, Same invoice date

29. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 29 Test “E” Same vendor number, Same invoice amount, Two invoice numbers the same considering letters and digits only (i.e. no special characters)

30. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 30 Test “F “ Same vendor, Same invoice amount, Same invoice number, if only letters are considered

31. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 31 Test “G” Same vendor number, Same invoice amount, Same invoice number, if only digits are considered (i.e. ignore letters and special characters, blanks, etc.)

32. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 32 Test “H” Same vendor, Same invoice amount, Invoice numbers are within the specified Levenshtein distance

33. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 33 Test “I” Same vendor, Same invoice amount, Invoice numbers are different due only to a transposition

34. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 34 Test “J” Same vendor number, Same invoice amount, Over 90% of the characters/digits in each invoice are the same Can specify different percentage Characters not necessarily in same sequence

35. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 35 Test “K” Same vendor, Same invoice date, Invoice amounts are within 2% of each other Can specify different percentage

36. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 36 Test “L” Same vendor, Same invoice date, First four leading characters of two invoices are the same Can use different number of leading digits Can specify different tests (LDO, DO, LO)

37. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 37 Test “M” Same vendor, Same invoice date, First four trailing characters of two invoices are the same Can use different number of leading digits Can specify different tests (LDO, DO, LO)

38. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 38 Test “N” Same vendor, Same invoice date, Same invoice amount Different invoices

39. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 39 Test “O” Same invoice number, Same invoice date, Same invoice amount, Different vendor

40. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 40 Test “P” Same invoice number, Same invoice date, Similar amount Measure as percentage Abs(invamt1-invamt2)/invamt1 Auditor specifies percentage, e.g. 2%

41. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 41 Test “Q” Same vendor, Same invoice amount, Same invoice number Similar invoice date Measured using Levenshtein distance Auditor specifies test distance

42. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 42 Test “R” Same vendor, Same invoice number, Similar date Measured using Levenshtein distance Auditor specifies distance

43. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 43 Test “S” Same invoice number, Same invoice date, Same invoice amount Similar vendor number Measured using Levenshtein distance Auditor specifies distance

44. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 44 Output Output is to a text file Import into Excel Pairs of rows

45. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 45 Limitations Currently handles only: Excel Access Text files (csv,tsv, etc.) No limit on rows (other than imposed by Excel) Has been tested using about 450,000 invoices Feasible to run on PC

46. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 46 Processing times File of 10,000 payments takes less than one minute Some tests take longer: Levenshtein distance Leading/trailing digits

47. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 47 Benchmark results 500,000 invoices tested 6,000 vendors Done on lap-top with dual 2.2 GHz About two minutes per test Larger volumes require longer YMMV

48. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 48 Duplicate Vendors Primary cause of duplicate payments Identified using two primary methods Exact – Same, same, different “Fuzzy” – Name matching

49. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 49 Same, Same, Different Same IRS Taxpayer ID (TIN) Different Vendor Number

50. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 50 Same, Same, Different Same Street Address Same City Same Zip Code Different Vendor Number

51. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 51 Same, Same, Different Same area code, Same contact number Same contact name Different Vendor Name

52. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 52 Same, Same, Different Same bank routing number Different vendor name/number

53. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 53 “Fuzzy” matching of vendor names Remove common terms (e.g. “corp”, “inc” etc.) Remove all but letters and numbers Compare every combination using- Match after removal of special characters Leading “N” characters Levenshtein distance

54. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 54 Fuzzy matching of TIN Transpositions Levenshtein distance

55. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 55 Fuzzy matching of Bank routing number Transpositions Levenshtein distance

56. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 56 Fuzzy matching of address Letters and digits only Levenshtein distance Transpositions Same characters

57. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 57 Benchmark Timings 10,000 vendors Access database CPU 1.5 GHz, memory 500MB Same, same, different - < 1 minute “Fuzzy” LDO – 20 seconds Leading – 2 minutes 10 seconds Levenshtein - ? (long time)

58. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 58 Continuous Monitoring Objectives Identify issues early Verify controls are working Quantify areas for audit

59. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 59 Monitor for potential duplicate payments Set up “duplicate payment test” directory Designate “log” file Run / refine tests Convert log file to “monitor” file Now simple to run tests on a cycle Just update file containing payments

60. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 60 Monitoring process Run all tests Review output Review for errors in current period Identify potential overpayments early

61. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 61 Other areas for monitoring Journal entries Expense reports P-card transactions Vendor payment trends Payroll Inventory Receivables Vendor master file

62. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 62 Example monitor processes Invoice payments – regression analysis Counts Totals Averages By month, week, quarter, etc. Policy compliance Requirements for PO

63. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 63 Monitoring (cont’d) Use of Benford’s Law Identification of credits not taken Top “10” Discounts not taken Vendor master – Checking for duplicates Checking for PO Boxes/ drop boxes Employee conflict of interest

64. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 64 Monitoring (cont’d) “Impossible” transaction conditions Data stratification Population statistics Quartiles Duplicate transactions Sequence gaps Same, same, different

65. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 65 More info Auditors Guide to Monitoring User Guide – Audit Commander User Guide – Audit Commander

66. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 66 Implementation Start small Low hanging fruit

67. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 67 Questions? General info 919-219-1622 E-Mail Thank you