1. © EZ-R Stats, LLC Duplicate Payments Slide 1 Auditing for Duplicate Payments A better way … Presentation of 6-18-2010

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 “Fuzzy” matches – invoice number

12. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 12 Date Transpositions 07/31/2010 vs. 07/13/2010 01/21/2009 vs. 02/11/2009

13. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 13 Data validation

14. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 14 Data valid?

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

16. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 16 Opening form

17. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 17 Fill in the blanks Select test to be performed

18. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 18 “Run” button

19. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 19 Results Text file Import into Excel

20. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 20 Processing volumes 500,000 invoices 40,000 vendors Process on lap-top with dual 2.2 GHz About two minutes per test

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

22. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 22 Pricing

23. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 23 Demo Excel workbook 10,417 Payments 19 tests

24. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 24 Workbook Excel workbook, 10,417 payments, 10 columns

25. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 25 Excel workbook contains four sheets

26. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 26 Opening menu – select “duplicate invoices”

27. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 27 Opening form

28. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 28 Select workbook and sheet

29. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 29 Select vendor column name

30. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 30 Select other column names and test to be performed

31. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 31 Click “run” button

32. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 32 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.

33. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 33 Test “A” All four key values equal Same vendor Same amount Same invoice date Same invoice number Note: case insensitive comparisons

34. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 34 Test “B” Same vendor, Same invoice number, Same invoice amount

35. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 35 Test “C” Same vendor number, Same invoice number, Same invoice date

36. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 36 Test “D” Same vendor, Same invoice amount, Same invoice date

37. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 37 Test “E” Same vendor number, Same invoice amount, Two invoice numbers the same considering letters and digits only (i.e. no special characters)

38. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 38 Test “F “ Same vendor, Same invoice amount, Same invoice number, if only letters are considered

39. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 39 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.)

40. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 40 Test “H” Same vendor, Same invoice amount, Invoice numbers are within the specified Levenshtein distance

41. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 41 Test “I” Same vendor, Same invoice amount, Invoice numbers are different due only to a transposition

42. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 42 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

43. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 43 Test “K” Same vendor, Same invoice date, Invoice amounts are within 2% of each other Can specify different percentage

44. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 44 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)

45. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 45 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)

46. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 46 Test “N” Same vendor, Same invoice date, Same invoice amount Different invoices

47. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 47 Test “O” Same invoice number, Same invoice date, Same invoice amount, Different vendor

48. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 48 Test “P” Same invoice number, Same invoice date, Similar amount Measure as percentage Abs(invamt1-invamt2)/invamt1 Auditor specifies percentage, e.g. 2%

49. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 49 Test “Q” Same vendor, Same invoice amount, Same invoice number Similar invoice date Measured using Levenshtein distance Auditor specifies test distance

50. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 50 Test “R” Same vendor, Same invoice number, Similar date Measured using Levenshtein distance Auditor specifies distance

51. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 51 Test “S” Same invoice number, Same invoice date, Same invoice amount Similar vendor number Measured using Levenshtein distance Auditor specifies distance

52. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 52 Output Output is to a text file Import into Excel Pairs of rows

53. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 53 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

54. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 54 Processing times File of 10,000 payments takes less than one minute Some tests take longer: Levenshtein distance Leading/trailing digits

55. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 55 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

56. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 56 Duplicate Vendors Primary cause of duplicate payments Identified using two primary methods Exact – Same, same, different “Fuzzy” – Name matching

57. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 57 Same, Same, Different Same IRS Taxpayer ID (TIN) Different Vendor Number

58. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 58 Same, Same, Different Same Street Address Same City Same Zip Code Different Vendor Number

59. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 59 Same, Same, Different Same area code, Same contact number Same contact name Different Vendor Name

60. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 60 Same, Same, Different Same bank routing number Different vendor name/number

61. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 61 “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

62. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 62 Fuzzy matching of TIN Transpositions Levenshtein distance

63. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 63 Fuzzy matching of Bank routing number Transpositions Levenshtein distance

64. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 64 Fuzzy matching of address Letters and digits only Levenshtein distance Transpositions Same characters

65. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 65 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)

66. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 66 Continuous Monitoring Objectives Identify issues early Verify controls are working Quantify areas for audit

67. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 67 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

68. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 68 Monitoring process Run all tests Review output Review for errors in current period Identify potential overpayments early

69. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 69 Other areas for monitoring Journal entries Expense reports P-card transactions Vendor payment trends Payroll Inventory Receivables Vendor master file

70. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 70 Example monitor processes Invoice payments – regression analysis Counts Totals Averages By month, week, quarter, etc. Policy compliance Requirements for PO

71. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 71 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

72. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 72 Monitoring (cont’d) “Impossible” transaction conditions Data stratification Population statistics Quartiles Duplicate transactions Sequence gaps Same, same, different

73. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 73 More info Auditors Guide to Monitoring User Guide – Audit Commander User Guide – Audit Commander

74. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 74 Implementation Start small Low hanging fruit

75. May 29, 2010 © 2010 EZ-R Stats, LLCSlide 75 Questions? General info 919-219-1622 E-Mail Thank you