Using Data Analysis to Detect Fraud - IIA Dallas Chapter

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Revenue106.Customers With No Terms Listed107.Customer Invoices With No Corresponding Sales Order108.Sales Stratified by Transparency International Corruption Index andCountry109.Sales to Government Owned Entities110.Actual Customer List111.Customers Matching Government Entities or Politically ExposedPeople112.Customer Accounts with a Credit Balance113.Sales by Country114.Sales to Country Different from Ship to Country115.Fixed Asset Additions Analysis116.Compare customers with common hotlist that have same addressUsing Data Analysis to Detect and Deter FraudPricewaterhouseCoopers117.Compare customers with project hotlist that have same name orsame addressMarch 2007Slide 42
Revenue121.Customers with same tax id122.Entities with same address123.Entities with same name124.Entities with same phone number125.Entities with same tax idUsing Data Analysis to Detect and Deter FraudPricewaterhouseCoopersMarch 2007Slide 43
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 and 20: Analytic TechniquesTesting across c
Page 21 and 22: Data Quality ChallengesNo uniquekey
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: Revenue91. Customer Ship Address Sa
Page 45 and 46: Inventory140.Unique General Ledger

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