anomaly detection petty

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Vendor Anomaly Detection Todd E. Petty 312.961.0111 Divine © 2002 Deloitte & Touche LLP. All rights reserved.

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  • 1. Vendor Anomaly Detection Todd E. Petty 312.961.0111 Divine 2002 Deloitte & Touche LLP. All rights reserved .

2. A/P Fraud Easily Concealed

  • Complex structure of large businesses makesfraud detection difficult.
    • Median loss due to accounts payable fraud is $250,000
      • Source: Association of Certified Fraud Examiners
  • How can fraud be found inmassive amountsof data?
    • One companys A/P profile
      • 30,000 vendors
      • Three million annual disbursement transactions @ $650 average per transaction
      • $2 BILLION in annual vendor disbursements
  • How many vendorsdoes your company really have?
    • Is IEE, Inc. the same vendor as I2E, Incorporated?
      • Inaccurate/sloppy data makes fraud easy to conceal
      • Vendor consolidationsaves money

3. Economics of Fraud ABC Company Revenue - $10 million Profit margin 10% 4. Anomaly DetectionBenefits

  • Identifiesfraud
    • Ghost vendors, ghost employees, employees as vendors, fraudulent invoices, etc.
      • Divine Security has identified fraud and duplicate payments in 100% ofanomaly detection engagements.
      • Uncover fraud before the costs reach damaging proportions.
  • Savesmoney
    • Identify vendor relationships
      • Larger discounts through volume purchases
    • Duplicate payments, overpayments, chargebacks, etc.
      • More than 90% of our clients have been able to cover our fees with just the duplicate payments we identified.
  • Identifies managementcontrol issues
    • Inactive suppliers, low value invoices, multiple accounts for supplier, incomplete/inaccurate records

5. Anomaly Detection Approach

  • Normalize and analyzeinternal company data
    • Vendor; accounts payable
  • Testit (using up to 100 Divine profiles) against
    • Selected internal data
      • Human resource; payroll
    • Selected external data
      • Social Security Number
      • Corporate Records
      • Dun & Bradstreet
  • Identify anomaliesthat may be indicative of fraud
    • Vendor phone number matches employee phone number
    • Vendor bank/account # matches employee bank/account #
    • Employee name matches vendor officer / director name
    • Employee SSN matches SSA paid death claim
    • Vendor address matches prison address

6. Process Overview - Functional Raw Internal Data Normalization Normalized Internal Data Profiles/Queries Anomalies Fraud Processed Result Create Use Profiles to Test Against External Data Internal Data Other Tests 7. Examples of A/P Anomalies

  • Vendor direct deposit disbursements and payroll direct deposit to same bank and bank account
  • Invoices with multiple purchase orders
  • Multiple purchase orders issued on the same day
  • Check amounts greater than purchase order amounts
  • Invoices with multiple checks issued
  • Ghost vendors
  • Employees as vendors
  • Ghost employees
  • Invoices with no purchase orders( see next page)

8. Questionable Payments? Invoices With No Supporting PO This section reflects INVOICES without any related POs POs INVOICES VENDOR 9. Anomaly Detection - Deliverables

  • Prioritized list of hits
    • Ranked by anomaly risk score
  • A summary of conflicts
    • Between data on different systems or from different sources
  • Identification of primary fraud risks
    • See next page

10. Primary Fraud Risks 11. Benfords Law 12. Employee Fraud 13. Employee Fraud 14. Employee Fraud 15. Employee Fraud 16. For more information, contact:

  • Todd E. Petty
  • Divine Security and Business Integrity Services
  • Forensic and Investigative Services
  • 312.961.0111