Using Data Analysis to Detect Fraud - IIA Dallas Chapter

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Analytic TechniquesClusteringGrouping of matched entities by property typeA=B, B=C; therefore, A=B=C- A = Bob Jones- B = Trucking Inc (Attn: Robert Jones)- C = NY Trucking IncBob JonesName, AddressTrucking Inc(Attn Robert Jones)Trucking Inc(Attn Robert Jones)NameNY Trucking IncUsing Data Analysis to Detect and Deter FraudPricewaterhouseCoopersMarch 2007Slide 20
Data Quality ChallengesNo uniquekeyNo consistentnaming conventionSpellingErrorsData EntryErrorsMissingValuesFreeFormTextID# Name Address City State Zip Note5154155 LaneM. Hitchcock 91017 th Street NW Washinton DC 20006A1617 LaneHitchcock, M., 910SeventeenthStreet NW DX 20006 021115146261 MyronStucky 110EyeSt. N.W. Washington DC 20005 CHKID87121 Stucky, Myron 110I Street 1 st floor DC FRAlert87458 Fisher &SmithInc 3197 th St. S.E. Washington DC 20003CompanyNamesBuriedInformationData inWrong FieldUsing Data Analysis to Detect and Deter FraudPricewaterhouseCoopersMarch 2007Slide 21
Page 3: “There is a tendency to mistake d
Page 6 and 7: Who detects fraud?Source:Pricewater
Page 8: Data Analysis FrameworkIndustry & C
Page 11 and 12: Analytic TechniquesCompare supplier
Page 13 and 14: Analytic TechniquesGhost employeesD
Page 15 and 16: Analytic TechniquesBenford’s LawU
Page 17 and 18: Analytic TechniquesHow do you analy
Page 19: Analytic TechniquesTesting across c
Page 23 and 24: Data Quality Challenges - Translite
Page 25 and 26: OutlineData Analysis FrameworkAnaly
Page 27 and 28: TechnologyA sampling of technologyC
Page 29 and 30: TRIA HighlightsTRIA applies machine
Page 31 and 32: TRIA HighlightDisbursements within
Page 33 and 34: Questions© 2007 PricewaterhouseCoo
Page 35 and 36: Accounts Payable1. Vendor Invoices
Page 37 and 38: Accounts Payable (Continued)31. Pay
Page 39 and 40: Accounts Payable (Continued)61. Ent
Page 41 and 42: Revenue91. Customer Ship Address Sa
Page 43 and 44: Revenue121.Customers with same tax
Page 45 and 46: Inventory140.Unique General Ledger

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