Using Data Analysis to Detect Fraud - IIA Dallas Chapter

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Revenue76.Segregation of Duties - Sales Orders and Customer Master File77.Duplicate Invoice Amounts to Customers78.Customers With No Phone Number Listed79.Unique General Ledger Entries80.Customers with an Address Match on the Common Hotlist81.Quantity of Products Invoiced Exceeds the Quantity of ProductsShipped82.Invoice Dollar Amount Exceeds Purchase Order Dollar Amount83.Round Dollar Customer Invoices84.Percentage of Customer Returns Compared to Customer Sales (byCustomer)85.Unusual Customer Trends (Based on Sales and Return Amounts)86.Discount Applied Differs from what is Stated in the Credit Terms87.Unauthorized Ship to AddressUsing Data Analysis to Detect and Deter FraudPricewaterhouseCoopersInvoice Date Prior To Ship DateMarch 2007Slide 40
Revenue91. Customer Ship Address Same as Employee Address92. Shipping Address is on the Common Hotlist93. Sales to Customers Not In Customer Master File94. Cash Receipts and Credit Memo Analysis by Customer95. Top Customer Case Receipts/Payments Analysis (80/20 Rule)96. Sales Invoice/Cash Receipts Stratification97. Working Capital Analysis on Customer Receipts98. Days Sale Outstanding by Terms and Working Capital Analysis99. Customer master file controls and maintenance100.Deviation of invoice terms from master file101.Credit memo analysis - customer102.Unauthorized price changes103.Credit Memo Analysis by CreatorUsing Data Analysis to Detect and Deter FraudPricewaterhouseCoopers104.Credit memo analysis - reason codeMarch 2007Slide 41
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: Accounts Payable (Continued)61. Ent
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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