1 / 84

Procurement Fraud

Procurement Fraud. November 11, 2008. Mike Blakley. Detection and Prevention. Session objectives. Current trends, techniques and best practices Understand statistical basis for analysis Procurement cards (p-cards) Understand use of Excel. Top Six Indicators That you might have a fraud.

erno
Download Presentation

Procurement Fraud

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. ProcurementFraud November 11, 2008 Mike Blakley Detection and Prevention

  2. Session objectives • Current trends, techniques and best practices • Understand statistical basis for analysis • Procurement cards (p-cards) • Understand use of Excel

  3. Top Six IndicatorsThat you might have a fraud • 6. System designed to do “three way match”, but only does two way • 5. Procurement software system doesn’t do a match • 4. When auditors ask to help them out, they point to the door • 3. No procurement software system • 2. Procurement clerk drives a Porsche • 1. Clerk’s kids drive Porsches between mountain home and beach home

  4. Overview • Fraud patterns detectable with digital analysis • Basis for digital analysis approach • Usage examples • Using Excel

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

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

  7. Regression Analysis Objective 1 • Stepwise to find relationships • Forwards • Backwards • Intervals • Confidence • Prediction

  8. Data outliers Objective 1 • Sometimes an “out and out Liar” • But how do you detect it?

  9. Data Outliers Objective 1 • Plot transportation costs vs. number of buses • “Drill down” on costs • Preventive maintenance • Fuel • Inspection

  10. Scatter plot with prediction and confidence intervals

  11. Medicare HIV Infusion Costs Objective 1 • CMS Report for 2005 • South Florida - $2.2 Billion • Rest of the country combined - $.1 Billion

  12. Pareto Chart Objective 1

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

  14. Objective 1 Managing the Business Risk of Fraud: A Practical Guide • IIA, AICPA and ACFE • Report issued 5/2008 • Section 5 – Fraud Detection

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

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

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

  18. Objective 1 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

  19. Peeling the Onion Objective 1c

  20. Fraud Pattern Detection Objective 1d

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

  22. Objective 1e Analytics – what is it? • Using software to: • Classify • Quantify • Compare • Both numeric and non-numeric data

  23. 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

  24. 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

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

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

  27. 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

  28. Objective 1 Summarized Objective 1 • Two brief examples • IIA Guidance Paper • “Top 10” Metrics • Process Overview

  29. Objective 1 - Summarized • Understand why and how • Understand statistical basis for quantifying differences • Identify ten general tools and techniques Next is the basis …

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

  31. Objective 3 Objective 2 Trapping anomalies

  32. 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

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

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

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

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

  37. 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

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

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

  40. Objective 2d Compute Cumulative Amount for each

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

  42. 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

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

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

  45. 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

  46. Objective 2d - KS Kolmogorov-Smirnov

  47. 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

  48. Objective 3 Fraud Pattern Detection

  49. What can be detected Objective 2 • Made up numbers – e.g. falsified inventory counts, tax return schedules

  50. Benford’s Law using Excel Objective 2 • 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)

More Related