Using Data Analysis to Detect Fraud - IIA Dallas Chapter

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TechnologyTRIA DemoUsing Data Analysis to Detect and Deter FraudPricewaterhouseCoopersMarch 2007Slide 32
Questions© 2007 PricewaterhouseCoopers LLP. All rights reserved. "PricewaterhouseCoopers" refers toPricewaterhouseCoopers LLP (a Delaware limited liability partnership) or, as the context requires, other memberfirms of PricewaterhouseCoopers International Ltd., each of which is a separate and independent legal entity.*connectedthinking is a trademark of PricewaterhouseCoopers LLP.
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: TRIA HighlightDisbursements within
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

Using Data Analysis to Detect Fraud - IIA Dallas Chapter

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