Fraud Detection Using A Database Platform

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Presentation made at the CCCFE conference of 2-23-2009 in Raleigh, NC

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Fraud Detection Using A Database Platform

  1. 1. Fraud Detection Using a Database Platform Mike Blakley Central Carolina Chapter of the Association of Certified Fraud February 23, 2009 Examiners Fraud Detetcion using a database platform EZ-R Stats, LLC
  2. 2. Session objectives Understand why and how 1. Understand statistical basis for 2. quantifying differences Identify ten general tools and 3. techniques Understand how pattern 4. detection fits in Fraud detection using a database platform EZ-R Stats, LLC
  3. 3. Session agenda and timings Managing the business risk of fraud (30 minutes)  Overview of statistical approach (10 min)  Discussion of databases (10 min)  Break (10 min)  Details of the approach (40 min)  Brief demo (5 min)  Open discussion and question and answer (15 min)  Fraud detection using a database platform EZ-R Stats, LLC
  4. 4. Handout (CD) CD with articles and software  PowerPoint presentation  More info at www.ezrstats.com  Fraud detection using a database platform EZ-R Stats, LLC
  5. 5. Optional quiz Test your understanding  Entirely optional  On home page under “events” – quiz  Results can be e-mailed  Fraud detection using a database platform EZ-R Stats, LLC
  6. 6. “Cockroach” theory of auditing If you spot one roach….  Fraud detection using a database platform EZ-R Stats, LLC
  7. 7. “Cockroach” theory of auditing There are probably 30  more that you don’t see… Fraud detection using a database platform EZ-R Stats, LLC
  8. 8. Statistics based “roach” hunting Many frauds coulda/woulda/shoulda been detected with analytics Fraud detection using a database platform EZ-R Stats, LLC
  9. 9. Overview Fraud patterns detectable with  digital analysis  Basis for digital analysis approach  Usage examples  Continuous monitoring  Business analytics Fraud detection using a database platform EZ-R Stats, LLC
  10. 10. Objective 1 The Why and How Three brief examples  ACFE/IIA/AICPA Guidance Paper  Practice Advisory 2320-1  Auditors “Top 10”  Process Overview  Who, What, Why, When & Where  Fraud detection using a database platform EZ-R Stats, LLC
  11. 11. Objective 1a Example 1 Wake County Transportation Fraud Supplier Kickback – School Bus  parts $5 million  Jail sentences  Period of years  Fraud detection using a database platform EZ-R Stats, LLC
  12. 12. Objective 1a Too little too late Understaffed internal audit  Software not used  Data on multiple platforms  Transaction volumes large  Fraud detection using a database platform EZ-R Stats, LLC
  13. 13. Objective 1a Preventable Need structured, objective  approach Let the data “talk to you”  Need efficient and effective  approach Fraud detection using a database platform EZ-R Stats, LLC
  14. 14. Objective 1 Regression Analysis Stepwise to find  relationships Forwards – Backwards – Intervals  Confidence – Prediction – Fraud detection using a database platform EZ-R Stats, LLC
  15. 15. Objective 1 Data outliers Sometimes an “out  and out Liar” But how do you  detect it? Fraud detection using a database platform EZ-R Stats, LLC
  16. 16. Objective 1 Data Outliers Plot transportation costs vs.  number of buses “Drill down” on costs  Preventive maintenance – Fuel – Inspection – Fraud detection using a database platform EZ-R Stats, LLC
  17. 17. Scatter plot with prediction and confidence intervals Fraud detection using a database platform EZ-R Stats, LLC
  18. 18. Objective 1a Example 2 Cost of six types of AIDS drugs Total Cost of AIDS Drugs 200 150 NDC1 Dollar Amount NDC2 100 NDC3 50 NDC4 NDC5 0 NDC6 NDC1 NDC2 NDC3 NDC4 NDC5 NDC6 Drug Type Fraud detection using a database platform EZ-R Stats, LLC
  19. 19. Objective 1 Medicare HIV Infusion Costs CMS Report for 2005  South Florida - $2.2 Billion  Rest of the country combined -  $.1 Billion Fraud detection using a database platform EZ-R Stats, LLC
  20. 20. Objective 1 Pareto Chart Medicare HIV Infusion Costs - 2005 ($Billions) data source: HHS CMS 120.0% 100.0% Annual Medicare Costs 80.0% Pct 60.0% Cum Pct 40.0% 20.0% 0.0% 1 3 5 7 9 11 13 15 County Fraud detection using a database platform EZ-R Stats, LLC
  21. 21. Objective 1a Example 2 Typical Prescription Patterns AIDS Drugs Prescription Patterns 60.0 NDC1 50.0 NDC2 40.0 Dollar Value NDC3 30.0 NDC4 20.0 NDC5 10.0 NDC6 0.0 Prov 1 Prov 2 Prov 3 Prov 4 Prov 5 Prov 6 Prescriber Fraud detection using a database platform EZ-R Stats, LLC
  22. 22. Objective 1a Example 2 Prescriptions by Dr. X Dr. X compared with Total Population 350 300 250 Dollar Amount 200 Population 150 100 Dr. X 50 0 NDC1 NDC2 NDC3 NDC4 NDC5 NDC6 Drug Type Fraud detection using a database platform EZ-R Stats, LLC
  23. 23. Objective 1a Example 2 Off-label use Serostim  Treat wasting syndrome, side effect of – AIDS, OR Used by body builders for recreational – purposes One physician prescribed $11.5 million – worth (12% of the entire state) Fraud detection using a database platform EZ-R Stats, LLC
  24. 24. Objective 1a Example 3 Revenue trends Overall Revenue Trend 1.2 1.15 Annual Billings 1.1 Overall 1.05 Linear (Overall) 1 0.95 0.9 2001 2002 2003 Calendar Year Fraud detection using a database platform EZ-R Stats, LLC
  25. 25. Example 3 Objective 1a Dental Billings Rapid Increase in Revenues 5 4 Annual Billings Billings A ($millions) 3 Billings B 2 Linear (Billings A) 1 0 2001 2002 2003 Calendar Year Fraud detection using a database platform EZ-R Stats, LLC
  26. 26. 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  Fraud detection using a database platform EZ-R Stats, LLC
  27. 27. Objective 1b Managing the Business Risk of Fraud: A Practical Guide ACFE, IIA and AICPA  Exposure draft issued 11/2007, final 5/2008 Section 4 – Fraud  Detection Fraud detection using a database platform EZ-R Stats, LLC
  28. 28. Guidance Paper Five Sections  Fraud Risk Governance – Fraud Risk Assessment – Fraud Prevention – Fraud Detection – Fraud Investigation and – corrective action Fraud detection using a database platform EZ-R Stats, LLC
  29. 29. Risk Governance Fraud risk management program  Written policy – management’s expectations  regarding managing fraud risk Fraud detection using a database platform EZ-R Stats, LLC
  30. 30. Risk Assessment Periodic review and assessment of potential  schemes and events Need to mitigate risk  Fraud detection using a database platform EZ-R Stats, LLC
  31. 31. Fraud Prevention Establish prevention techniques  Mitigate possible impact on the organization  Fraud detection using a database platform EZ-R Stats, LLC
  32. 32. Fraud Detection Establish detection techniques for fraud  “Back stop” where preventive measures fail,  or Unmitigated risks are realized  Fraud detection using a database platform EZ-R Stats, LLC
  33. 33. Fraud Investigation and Corrective Action Reporting process to solicit input on fraud  Coordinated approach to investigation  Use of corrective action  Fraud detection using a database platform EZ-R Stats, LLC
  34. 34. “60 Minutes” – “World of Trouble” 2/15/09 – Scott Pelley  Fraud Risk Governance – “one grand wink-wink, – nod-nod “ Fraud Risk Assessment - categorically false – Fraud Prevention – “my husband passed away” – Fraud Detection - We didn't know? Never saw one. – Fraud Investigation and corrective action - Pick-A- – Payment losses $36 billion Fraud detection using a database platform EZ-R Stats, LLC
  35. 35. Objective 1b Section 4 – Fraud Detection Detective Controls  Process Controls  Anonymous Reporting  Internal Auditing  Proactive Fraud Detection  Fraud detection using a database platform EZ-R Stats, LLC
  36. 36. Objective 1b Proactive Fraud Detection Data Analysis to identify:  – Anomalies – Trends – Risk indicators Fraud detection using a database platform EZ-R Stats, LLC
  37. 37. Fraud Detective Controls Operate in the background  Not evident in everyday business  environment These techniques usually –  Occur in ordinary course of business – Corroboration using external information – Automatically communicate deficiencies – Use results to enhance other controls – Fraud detection using a database platform EZ-R Stats, LLC
  38. 38. Examples of detective controls Whistleblower hot-lines (DHHS and OSA  have them) Process controls (Medicaid audits and edits)  Proactive fraud detection procedures  Data analysis – Continuous monitoring – Benford’s Law – Fraud detection using a database platform EZ-R Stats, LLC
  39. 39. Objective 1b Specific Examples Cited Journal entries – suspicious  transactions Identification of relationships  Benford’s Law  Continuous monitoring  Fraud detection using a database platform EZ-R Stats, LLC
  40. 40. 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  Fraud detection using a database platform EZ-R Stats, LLC
  41. 41. Continuous Monitoring of Fraud Detection Organization should develop ongoing  monitoring and measurements Establish measurement criteria (and  communicate to Board) Measurable criteria include:  Fraud detection using a database platform EZ-R Stats, LLC
  42. 42. Measurable Criteria – number of fraud allegations  fraud investigations resolved  Employees attending annual ethics course  Whistle blower allegations  Messages supporting ethical behavior  delivered by executives Vendors signing ethical behavior standards  Fraud detection using a database platform EZ-R Stats, LLC
  43. 43. Management ownership of each technique implemented Each process owner should:  Evaluate effectiveness of technique regularly – Adjust technique as required – Document adjustments – Report modifications needed for techniques which – become less effective Fraud detection using a database platform EZ-R Stats, LLC
  44. 44. Practice Advisory 2320-1 Analysis and Evaluation International standards for the professional  practice of Internal Auditing Analytical audit procedures  Efficient and effective – Useful in detecting – Differences that are not expected  Potential errors  Potential irregularities  Fraud detection using a database platform EZ-R Stats, LLC
  45. 45. Analytical Audit Procedures May include  – Study of relationships – Comparison of amounts with similar information in the organization – Comparison of amounts with similar information in the industry Fraud detection using a database platform EZ-R Stats, LLC
  46. 46. Analytical audit procedures Performed using monetary amounts, physical  quantities, ratios or percentages Ratio, trend and regression analysis  Period to period comparisons  Auditors should use analytical audit  procedures in planning the engagement Fraud detection using a database platform EZ-R Stats, LLC
  47. 47. Factors to consider Significance of the area being audited  Assessment of risk  Adequacy of system of internal control  Availability and reliability of information  Extent to which procedures provide support  for engagement results Fraud detection using a database platform EZ-R Stats, LLC
  48. 48. Objective 1c Peeling the Onion Fraud Items Possible Error Conditions Population as Whole Fraud detection using a database platform EZ-R Stats, LLC
  49. 49. Objective 1d Fraud Pattern Detection Round Numbers Benford’s Law Market Basket Stratification Gaps Target Group Trend Line Univariate Duplicates Holiday Day of Week Fraud detection using a database platform EZ-R Stats, LLC
  50. 50. Objective 1e Digital Analysis (5W) Who   What  Why  Where  When Fraud detection using a database platform EZ-R Stats, LLC
  51. 51. 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 Fraud detection using a database platform EZ-R Stats, LLC
  52. 52. Objective 1e What - Digital Analysis Using software to:  Classify – Quantify – Compare – Both numeric and non-numeric  data Fraud detection using a database platform EZ-R Stats, LLC
  53. 53. 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  Fraud detection using a database platform EZ-R Stats, LLC
  54. 54. 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  Fraud detection using a database platform EZ-R Stats, LLC
  55. 55. Objective 1e Why - Disadvantages Costly (time and software costs)  Learning curve  Requires specialized knowledge  Fraud detection using a database platform EZ-R Stats, LLC
  56. 56. Objective 1e When to Use Digital Analysis Traditional – intermittent (one off)  Trend is to use it as often as possible  Continuous monitoring  Scheduled processing  Fraud detection using a database platform EZ-R Stats, LLC
  57. 57. 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 – Fraud detection using a database platform EZ-R Stats, LLC
  58. 58. Objective 1 Objective 1 Summarized Three brief examples  CFE Guidance Paper  “Top 10” Metrics  Process Overview  Who, What, Why, When & Where  Fraud detection using a database platform EZ-R Stats, LLC
  59. 59. Objective 1 - Summarized Understand why and how 1. Understand statistical basis for quantifying 2. differences Identify ten general tools and techniques 3. Understand use of Excel 4. How pattern detection fits in 5. Next is the basis … Fraud detection using a database platform EZ-R Stats, LLC
  60. 60. Objective 2 Basis for Pattern Detection Analytical review  Isolate the “significant few”  Detection of errors  Quantified approach  Fraud detection using a database platform EZ-R Stats, LLC
  61. 61. 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  Fraud detection using a database platform EZ-R Stats, LLC
  62. 62. Objective 2a Quantified Approach Based on measureable  differences  Population vs. Group  “Shotgun” technique Fraud detection using a database platform EZ-R Stats, LLC
  63. 63. Objective 2a Detection of Fraud Characteristics Something is different than expected  Fraud detection using a database platform EZ-R Stats, LLC
  64. 64. Objective 2b Fraud patterns Common theme – “something is  different”  Groups  Group pattern is different than overall population Fraud detection using a database platform EZ-R Stats, LLC
  65. 65. Objective 2c Measurement Basis  Transaction counts  Transaction amounts Fraud detection using a database platform EZ-R Stats, LLC
  66. 66. Objective 2d A few words about statistics (the “s” word) Detailed knowledge of statistics not  necessary Software packages do the “number-  crunching” Statistics used only to highlight  potential errors/frauds Not used for quantification  Fraud detection using a database platform EZ-R Stats, LLC
  67. 67. 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  Fraud detection using a database platform EZ-R Stats, LLC
  68. 68. Demo in Excel of the process Based roughly on the Wake County  Transportation fraud Illustrates how the process works, using  Excel Fraud detection using a database platform EZ-R Stats, LLC
  69. 69. Objective 2d Histograms Attributes tallied and categorized into “bins”  Counts or sums of amounts  Fraud detection using a database platform EZ-R Stats, LLC
  70. 70. Objective 2d Two histograms obtained Population and group  Population Group 700 80 600 70 60 500 50 400 40 300 30 200 20 100 10 0 0 Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 Fraud detection using a database platform EZ-R Stats, LLC
  71. 71. Objective 2d Histograms Attributes tallied and categorized into “bins”  Counts or sums of amounts  Fraud detection using a database platform EZ-R Stats, LLC
  72. 72. Objective 2d Compute Cumulative Amount for each Count by Month Cum Pct 80 120.0% 70 100.0% 60 50 80.0% Count 40 60.0% 30 20 40.0% 10 20.0% 0 Au 07 Ju 7 Fe 7 Ju 7 Ap 7 07 M 07 O7 De 7 No 07 Se 7 0.0% M7 -0 -0 0 0 0 n-0 0 r-0 l- c- p- v- n- b- ct- g- ay ar Ja 7 07 07 07 7 07 l-0 -0 M onth p- - n- - ov ay ar Ju Ja Se M N M Fraud detection using a database platform EZ-R Stats, LLC
  73. 73. Objective 2d Are the histograms different? Two statistical measures of  difference Chi Squared (counts)  K-S (distribution)  Both yield a difference metric  Fraud detection using a database platform EZ-R Stats, LLC
  74. 74. 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 Fraud detection using a database platform EZ-R Stats, LLC
  75. 75. Objective 2d Chi Squared Table of Counts  Degrees of Freedom  Chi Squared Value  P-statistic  Computationally intensive  Fraud detection using a database platform EZ-R Stats, LLC
  76. 76. Objective 2d Kolmogorov-Smirnov Two Russian  mathematicians  Comparison of distributions  Metric is the “d-statistic” Fraud detection using a database platform EZ-R Stats, LLC
  77. 77. Objective 2d How is K-S test done? Four step process  For each cluster element 1. determine percentage Then calculate cumulative 2. percentage Compare the differences in 3. cumulative percentages Identify the largest difference 4. Fraud detection using a database platform EZ-R Stats, LLC
  78. 78. Objective 2d - KS Kolmogorov-Smirnov Fraud detection using a database platform EZ-R Stats, LLC
  79. 79. 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  Fraud detection using a database platform EZ-R Stats, LLC
  80. 80. Objective e Auditor’s “Top 10” Metrics Outliers / Variability 1. Stratification 2. Day of Week 3. Round Numbers 4. Made Up Numbers 5. Market basket 6. Trends 7. Gaps 8. Duplicates 9. Dates 10. Fraud detection using a database platform EZ-R Stats, LLC
  81. 81. 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  Fraud detection using a database platform EZ-R Stats, LLC
  82. 82. Objective 2 - Summarized Understand why and how 1. Understand statistical basis for quantifying 2. differences Identify ten general tools and techniques 3. Understand examples done using Excel 4. How pattern detection fits in 5. Next are the metrics … Fraud detection using a database platform EZ-R Stats, LLC
  83. 83. It’s that time! Session Break! Fraud detection using a database platform EZ-R Stats, LLC
  84. 84. Objective 3 The “Top 10” Metrics Overview  Explain Each Metric  Examples of what it can detect  How to assess results  Fraud detection using a database platform EZ-R Stats, LLC
  85. 85. Objective 3 Trapping anomalies Fraud detection using a database platform EZ-R Stats, LLC
  86. 86. Objective 3 Fraud Pattern Detection Round Numbers Benford’s Law Market Basket Stratification Gaps Target Group Trend Line Univariate Duplicates Holiday Day of Week Fraud detection using a database platform EZ-R Stats, LLC
  87. 87. 1 - Outliers Outliers / Variability Outliers are amounts which are significantly different from the rest of the population Fraud detection using a database platform EZ-R Stats, LLC
  88. 88. 1 - Outliers Outliers / Variability Charting (visual)  Software to analyze “z-scores”  Top and Bottom 10, 20 etc.  High and low variability (coefficient  of variation) Fraud detection using a database platform EZ-R Stats, LLC
  89. 89. 1 - Outliers Drill down to the group level Basic statistics  – Minimum, maximum and average – Variability Sort by statistic of interest  – Variability (coefficient of variation) – Maximum, etc. Fraud detection using a database platform EZ-R Stats, LLC
  90. 90. 1 - Outliers Example Results Provider N Coeff Var 3478421 3,243 342.23 2356721 4,536 87.23 3546789 3,421 23.25 5463122 2,311 18.54 Two providers (3478421 and 2356721) had significantly more variability in the amounts of their claims than all the rest. Fraud detection using a database platform EZ-R Stats, LLC
  91. 91. Next Metric Outliers 1. Stratification 2. Day of Week 3. Round Numbers 4. Made Up Numbers 5. Market basket 6. Trends 7. Gaps 8. Duplicates 9. Dates 10. Fraud detection using a database platform EZ-R Stats, LLC
  92. 92. 2 - Stratification Unusual stratification patterns Do you know how your data looks? Fraud detection using a database platform EZ-R Stats, LLC
  93. 93. 2 - Stratification Stratification - How Charting (visual)   Chi Squared  Kolmogorov-Smirnov  By groups Fraud detection using a database platform EZ-R Stats, LLC
  94. 94. 2 – Stratification Purpose / types of errors Transactions out of the ordinary  “Up-coding” insurance claims  “Skewed” groupings  Based on either count or amount  Fraud detection using a database platform EZ-R Stats, LLC
  95. 95. 2 – Stratification The process? Stratify the entire population into 1. “bins” specified by auditor Same stratification on each group 2. (e.g. vendor) Compare the group tested to the 3. population Obtain measure of difference for each 4. group Sort descending on difference 5. measure Fraud detection using a database platform EZ-R Stats, LLC
  96. 96. 2 – Stratification Units of Service Stratified - Example Results Provider N Chi Sq D-stat 2735211 6,011 7,453 0.8453 4562134 8,913 5,234 0.7453 4321089 3,410 342 0.5231 4237869 2,503 298 0.4632 Two providers (2735211 and 4562134) are shown to be much different from the overall population (as measured by Chi Square). Fraud detection using a database platform EZ-R Stats, LLC
  97. 97. Next Metric Outliers 1. Stratification 2. Day of Week 3. Round Numbers 4. Made Up Numbers 5. Market basket 6. Trends 7. Gaps 8. Duplicates 9. Dates 10. Fraud detection using a database platform EZ-R Stats, LLC
  98. 98. 3 – Day of Week Day of Week Activity on weekdays  Activity on weekends  Peak activity mid to late week  Fraud detection using a database platform EZ-R Stats, LLC
  99. 99. 3 – Day of Week Purpose / Type of Errors Identify unusually high/low  activity on one or more days of week Dentist who only handled  Medicaid on Tuesday Office is empty on Friday  Fraud detection using a database platform EZ-R Stats, LLC
  100. 100. How it is done? Programmatically check entire population  Obtain counts and sums by day of week  (1-7) Prepare histogram  For each group do the same procedure  Compare the two histograms  Sort descending by metric (chi square/d-  stat) Fraud detection using a database platform EZ-R Stats, LLC
  101. 101. 3 – Day of Week Day of Week - Example Results Provider N Chi Sq D-stat 2735211 5,404 12,435 0.9802 4562134 5,182 7,746 0.8472 4321089 5,162 87 0.321 4237869 7,905 56 0.2189 Provider 2735211 only provided service for Medicaid on Tuesdays. Provider 4562134 was closed on Thursdays and Fridays. Fraud detection using a database platform EZ-R Stats, LLC
  102. 102. Next Metric Outliers 1. Stratification 2. Day of Week 3. Round Numbers 4. Made Up Numbers 5. Market basket 6. Trends 7. Gaps 8. Duplicates 9. Dates 10. Fraud detection using a database platform EZ-R Stats, LLC
  103. 103. 4 – Round Numbers Round Numbers It’s about…. Estimates! Fraud detection using a database platform EZ-R Stats, LLC
  104. 104. 4 – Round Numbers Purpose / Type of Errors Isolate estimates  Highlight account numbers in  journal entries with round numbers Split purchases (“under the radar”)  Which groups have the most  estimates Fraud detection using a database platform EZ-R Stats, LLC
  105. 105. 4 – Round Numbers Round numbers Classify population amounts  – $1,375.23 is not round – $5,000 is a round number – type 3 (3 zeros) – $10,200 is a round number type 2 (2 zeros) Quantify expected vs. actual (d-statistic)  Generally represents an estimate  Journal entries  Fraud detection using a database platform EZ-R Stats, LLC
  106. 106. 4 – Round Numbers Round Numbers in Journal Entries - Example Results Account N Chi Sq D-stat 2735211 4,136 54,637 0.9802 4562134 833 35,324 0.97023 4321089 8,318 768 0.321 4237869 9,549 546 0.2189 Two accounts, 2735211 and 4562134 have significantly more round number postings than any other posting account in the journal entries. Fraud detection using a database platform EZ-R Stats, LLC
  107. 107. Next Metric Outliers 1. Stratification 2. Day of Week 3. Round Numbers 4. Made Up Numbers 5. Market basket 6. Trends 7. Gaps 8. Duplicates 9. Dates 10. Fraud detection using a database platform EZ-R Stats, LLC
  108. 108. 5 – Made up numbers Made up Numbers Curb stoning Imaginary numbers Benford’s Law Fraud detection using a database platform EZ-R Stats, LLC
  109. 109. 5 – Made Up Numbers What can be detected Made up numbers –  e.g. falsified inventory counts, tax return schedules Fraud detection using a database platform EZ-R Stats, LLC
  110. 110. 5 – Made Up Numbers Benford’s Law using Excel Basic formula is “=log(1+(1/N))”  Workbook with formulae available at  http://tinyurl.com/4vmcfs Obtain leading digits using “Left”  function, e.g. left(Cell,1) Fraud detection using a database platform EZ-R Stats, LLC
  111. 111. 5 – Made Up Numbers Made up numbers Benford’s Law  Check Chi Square and d-statistic  First 1,2,3 digits  Last 1,2 digits  Second digit  Sources for more info  Fraud detection using a database platform EZ-R Stats, LLC
  112. 112. 5 – Made Up Numbers How is it done? Decide type of test – (first 1-3 digits, last  1-2 digit etc) For each group, count number of  observations for each digit pattern Prepare histogram  Based on total count, compute expected  values For the group, compute Chi Square and  d-stat Sort descending by metric (chi square/d-  stat) Fraud detection using a database platform EZ-R Stats, LLC
  113. 113. 5 – Made Up Numbers Invoice Amounts tested with Benford’s law - Example Results Store Hi Digit Chi Sq D-stat 324 79 5,234 0.9802 563 89 4,735 0.97023 432 23 476 0.321 217 74 312 0.2189 During tests of invoices by store, two stores, 324 and 563 have significantly more differences than any other store as measured by Benford’s Law. Fraud detection using a database platform EZ-R Stats, LLC
  114. 114. Next Metric Outliers 1. Stratification 2. Day of Week 3. Round Numbers 4. Made Up Numbers 5. Market basket 6. Trends 7. Gaps 8. Duplicates 9. Dates 10. Fraud detection using a database platform EZ-R Stats, LLC
  115. 115. 6 – Market Basket Market Basket Medical “Ping ponging”  Pattern associations  Apriori program  References at end of slides  Apriori – Latin a (from) priori  (former) Deduction from the known  Fraud detection using a database platform EZ-R Stats, LLC
  116. 116. 6 – Market basket Purpose / Type of Errors Unexpected patterns and  associations Based on “market basket” concept  Unusual combinations of diagnosis  code on medical insurance claim Fraud detection using a database platform EZ-R Stats, LLC
  117. 117. 6 – Market basket Market Basket JE Accounts   JE Approvals  Credit card fraud in Japan – taxi and ATM Fraud detection using a database platform EZ-R Stats, LLC
  118. 118. 6 – Market basket How is it done? First, identify groups, e.g. all  medical providers for a patient Next, for each provider, assign a  unique integer value Create a text file containing the  values Run “apriori” analysis  Fraud detection using a database platform EZ-R Stats, LLC
  119. 119. 6 – Market basket Apriori outputs For each unique value, probability of  other values If you see Dr. Jones, you will also  see Dr. Smith (80% probability) If you see a JE to account ABC, there  will also an entry to account XYZ (30%) Fraud detection using a database platform EZ-R Stats, LLC
  120. 120. Next Metric Outliers 1. Stratification 2. Day of Week 3. Round Numbers 4. Made Up Numbers 5. Market basket 6. Trends 7. Gaps 8. Duplicates 9. Dates 10. Fraud detection using a database platform EZ-R Stats, LLC
  121. 121. 7 - Trends Trend Busters Does the pattern make sense? ACME Technology 30,000 25,000 20,000 Amount Sales 15,000 Em ployee Count 10,000 5,000 0 7 8 7 M8 7 07 7 08 7 -0 -0 -0 -0 l-0 0 0 v- n- n- p- ay ay ar ar Ju No Ja Ja Se M M M Date Fraud detection using a database platform EZ-R Stats, LLC
  122. 122. 7 – Trends Trend Busters Linear regression  Sales are up, but cost of goods sold is  down “Spikes”  Fraud detection using a database platform EZ-R Stats, LLC
  123. 123. 7 – Trends Purpose / Type of Errors Identify trend lines, slopes,  etc. Correlate trends  Identify anomalies  Key punch errors where  amount is order of magnitude Fraud detection using a database platform EZ-R Stats, LLC
  124. 124. 7 – Trends Linear Regression  Test relationships (e.g. invoice amount and sales tax)  Perform multi-variable analysis Fraud detection using a database platform EZ-R Stats, LLC
  125. 125. 7 – Trends How is it done? Estimate linear trends using “best  fit” Measure variability (standard  errors) Measure slope  Sort descending by slope,  variability, etc. Fraud detection using a database platform EZ-R Stats, LLC
  126. 126. 7 – Trends Trend Lines by Account - Example Results Account N Slope Std Err 32451 18 1.230 0.87 43517 17 1.070 4.3 32451 27 1.023 0.85 43517 32 1.010 0.36 43870 23 0.340 2.36 54630 56 -0.560 1.89 Generally the trend is gently sloping up, but two accounts (43870 and 54630) are different. Fraud detection using a database platform EZ-R Stats, LLC
  127. 127. Scatter plot with prediction and confidence intervals Fraud detection using a database platform EZ-R Stats, LLC
  128. 128. Next Metric Outliers 1. Stratification 2. Day of Week 3. Round Numbers 4. Made Up Numbers 5. Market basket 6. Trends 7. Gaps 8. Duplicates 9. Dates 10. Fraud detection using a database platform EZ-R Stats, LLC
  129. 129. 8 - Gaps Numeric Sequence Gaps What’s there is interesting, what’s not there is critical … Fraud detection using a database platform EZ-R Stats, LLC
  130. 130. 8 – Gaps Purpose / Type of Errors Missing documents (sales, cash,  etc.) Inventory losses (missing receiving  reports) Items that “walked off”  Fraud detection using a database platform EZ-R Stats, LLC
  131. 131. 8 – Gaps How is it done? Check any sequence of numbers  supposed to be complete, e.g. Cash receipts  Sales slips  Purchase orders  Fraud detection using a database platform EZ-R Stats, LLC
  132. 132. 8 – Gaps Gaps Using Excel Excel – sort and check  Excel formula  Sequential numbers and dates  Fraud detection using a database platform EZ-R Stats, LLC
  133. 133. 8 – Gaps Gap Testing - Example Results Start End Missing 10789 10791 1 12523 12526 2 17546 17548 1 Four check numbers are missing. Fraud detection using a database platform EZ-R Stats, LLC
  134. 134. Next Metric Outliers 1. Stratification 2. Day of Week 3. Round Numbers 4. Made Up Numbers 5. Market basket 6. Trends 7. Gaps 8. Duplicates 9. Dates 10. Fraud detection using a database platform EZ-R Stats, LLC
  135. 135. 9 - Duplicates Duplicates Why is there more than one? Same, Same, Same, and Same, Same, Different Fraud detection using a database platform EZ-R Stats, LLC
  136. 136. 9 – Duplicates Two types of (related) tests Same items – same vendor, same invoice  number, same invoice date, same amount Different items – same employee name,  same city, different social security number Fraud detection using a database platform EZ-R Stats, LLC
  137. 137. 9 - Duplicates Duplicate Payments High payback area   “Fuzzy” logic Overriding software  controls Fraud detection using a database platform EZ-R Stats, LLC
  138. 138. 9 - Duplicates Fuzzy matching with software Levenshtein distance  Soundex  “Like” clause in SQL  Russian Regular expression physicist  testing in SQL Vendor/employee  situations Fraud detection using a database platform EZ-R Stats, LLC
  139. 139. 9 - Duplicates How is it done? First, sort file in sequence for  testing Compare items in consecutive  rows Extract exceptions for follow-up  Fraud detection using a database platform EZ-R Stats, LLC
  140. 140. 9 - Duplicates Possible Duplicates - Example Results Vendor Invoice Date Invoice Count Amount 10245 6/15/2007 3,544.78 4 10245 8/31/2007 2,010.37 2 17546 2/12/2007 1,500.00 2 Five invoices may be duplicates. Fraud detection using a database platform EZ-R Stats, LLC
  141. 141. Next Metric Outliers 1. Stratification 2. Day of Week 3. Round Numbers 4. Made Up Numbers 5. Market basket 6. Trends 7. Gaps 8. Duplicates 9. Dates 10. Fraud detection using a database platform EZ-R Stats, LLC
  142. 142. 10 - Dates Date Checking If we’re closed, why is there … Adjusting journal entry? Receiving report? Payment issued? Fraud detection using a database platform EZ-R Stats, LLC
  143. 143. 10 – Dates Holiday Date Testing Red Flag indicator  Fraud detection using a database platform EZ-R Stats, LLC
  144. 144. 10 – Dates Date Testing challenges Difficult to determine  Floating holidays –  Friday, Saturday, Sunday, Monday Fraud detection using a database platform EZ-R Stats, LLC
  145. 145. 10 – Dates Typical audit areas Journal entries  Employee expense  reports Business telephone calls  Invoices  Receiving reports  Purchase orders  Fraud detection using a database platform EZ-R Stats, LLC
  146. 146. 10 – Dates Determination of Dates Transactions when business is  closed Federal Office of Budget  Management An excellent fraud indicator in  some cases Fraud detection using a database platform EZ-R Stats, LLC
  147. 147. 10 – Dates Holiday Date Testing Identifying holiday  dates: – Error prone – Tedious U.S. only  Fraud detection using a database platform EZ-R Stats, LLC
  148. 148. 10 – Dates Federal Holidays Established by Law  Ten dates  Specific date (unless  weekend), OR Floating holiday  Fraud detection using a database platform EZ-R Stats, LLC
  149. 149. 10 – Dates Federal Holiday Schedule Office of Personnel Management  Example of specific date – Independence  Day, July 4th (unless weekend) Example of floating date – Martin Luther  King’s birthday (3rd Monday in January) Floating – Thanksgiving – 4th Thursday in  November Fraud detection using a database platform EZ-R Stats, LLC
  150. 150. 10 – Dates How it is done? Programmatically count holidays for  entire population For each group, count holidays  Compare the two histograms (group  and population) Sort descending by metric (chi  square/d-stat) Fraud detection using a database platform EZ-R Stats, LLC
  151. 151. 10 – Dates Holiday Counts - Example Results Employee N Chi Sq D-stat Number 10245 37 5,234 0.9802 32325 23 4,735 0.97023 17546 18 476 0.321 24135 34 312 0.2189 Two employees (10245 and 32325) were “off the chart” in terms of expense amounts incurred on a Federal Holiday. Fraud detection using a database platform EZ-R Stats, LLC
  152. 152. Objective 3 The “Top 10” Metrics Overview  Explain Each Metric  Examples of what it can detect  How to assess results  Fraud detection using a database platform EZ-R Stats, LLC
  153. 153. Objective 3 - Summarized Understand why and how 1. Understand statistical basis for quantifying 2. differences Identify ten general tools and techniques 3. Understand examples done using Excel 4. How pattern detection fits in 5. Next – using Excel … Fraud detection using a database platform EZ-R Stats, LLC
  154. 154. Objective 4 Use of Excel Built-in functions  Add-ins  Macros  Database access  Fraud detection using a database platform EZ-R Stats, LLC
  155. 155. Objective 4 Excel templates Variety of tests  Round numbers – Benford’s Law – Outliers – Etc. – Fraud detection using a database platform EZ-R Stats, LLC
  156. 156. Objective 4 Excel – Univariate statistics Work with Ranges  =sum, =average, =stdevp  =largest(Range,1),  =smallest(Range,1) =min, =max, =count  Tools | Data Analysis | Descriptive  Statistics Fraud detection using a database platform EZ-R Stats, LLC
  157. 157. Objective 4 Excel Histograms Tools | Data Analysis | Histogram  Bin Range  Data Range  Fraud detection using a database platform EZ-R Stats, LLC
  158. 158. Objective 4 Excel Gaps testing Sort by sequential value  =if(thiscell-lastcell <>  1,thiscell-lastcell,0) Copy/paste special  Sort  Fraud detection using a database platform EZ-R Stats, LLC
  159. 159. Objective 4 Detecting duplicates with Excel Sort by sort values  =if testing  =if(=and(thiscell=lastcell, etc.))  Fraud detection using a database platform EZ-R Stats, LLC
  160. 160. Objective 4 Performing audit tests with macros Repeatable process  Audit standardization  Learning curve  Streamlining of tests  More efficient and effective  Examples -  http://ezrstats.com/Macros/home.html Fraud detection using a database platform EZ-R Stats, LLC
  161. 161. Objective 4 Using database audit software Many “built-in” functions right off the shelf  with SQL Control totals  Exception identification  “Drill down”  Quantification  June 2008 article in the EDP Audit &  Control Journal (EDPACS) “SQL as an audit tool” http://ezrstats.com/doc/SQL_As_An_Audit_Tool.pdf  Fraud detection using a database platform EZ-R Stats, LLC
  162. 162. Objective 4 Use of Excel Built-in functions  Add-ins  Macros  Database access  Fraud detection using a database platform EZ-R Stats, LLC
  163. 163. Objective 4 - Summarized Understand why and how 1. Understand statistical basis for quantifying 2. differences Identify ten general tools and techniques 3. Understand examples done using Excel 4. How Pattern Detection fits in 5. Next – Fit … Fraud detection using a database platform EZ-R Stats, LLC
  164. 164. Objective 5 How Pattern Detection Fits In Business Analytics  Fraud Pattern Detection  Continuous monitoring  Fraud detection using a database platform EZ-R Stats, LLC
  165. 165. Objective 5 Where does Fraud Pattern Detection fit in? Right in the middle Business Analytics  Fraud Pattern Detection  Continuous fraud pattern  detection Continuous Monitoring  Fraud detection using a database platform EZ-R Stats, LLC
  166. 166. Objective 5 Business Analytics Fraud analytics -> business  analytics Business analytics -> fraud  analytics Fraud detection using a database platform EZ-R Stats, LLC
  167. 167. Objective 5 Role in Continuous Monitoring (CM) Fraud analytics can feed (CM)  Continuous fraud pattern detection  Use output from CM to tune fraud  pattern detection Fraud detection using a database platform EZ-R Stats, LLC
  168. 168. Objective 5 - Summarized Understand why and how 1. Understand statistical basis for quantifying 2. differences Identify ten general tools and techniques 3. Understand use of Excel 4. How pattern detection fits in 5. Next: Links … Fraud detection using a database platform EZ-R Stats, LLC
  169. 169. Links for more information Kolmogorov-Smirnov  http://tinyurl.com/y49sec  Benford’s Law http://tinyurl.com/3qapzu  Chi Square tests http://tinyurl.com/43nkdh  Continuous monitoring  http://tinyurl.com/3pltdl Fraud detection using a database platform EZ-R Stats, LLC
  170. 170. Market Basket Apriori testing for “ping ponging”  Temple University  http://tinyurl.com/5vax7r Apriori program (“open source”)  http://tinyurl.com/5qehd5 Article – “Medical ping ponging”  http://tinyurl.com/5pzbh4 Fraud detection using a database platform EZ-R Stats, LLC
  171. 171. Excel macros used in auditing Excel as an audit software  http://tinyurl.com/6h3ye7 Selected macros -  http://ezrstats.com/Macros/home.html Spreadsheets forever -  http://tinyurl.com/5ppl7t Fraud detection using a database platform EZ-R Stats, LLC
  172. 172. Questions? Fraud detection using a database platform EZ-R Stats, LLC
  173. 173. Contact info  Phone: (919)-219-1622  E-mail: Mike.Blakley@ezrstats.com  Blog: http://blog.ezrstats.com Fraud detection using a database platform EZ-R Stats, LLC

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