anomaly detection petty
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a process I invented years ago I share with everyoneTRANSCRIPT
- 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.
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- Median loss due to accounts payable fraud is $250,000
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- Source: Association of Certified Fraud Examiners
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- How can fraud be found inmassive amountsof data?
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- One companys A/P profile
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- 30,000 vendors
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- Three million annual disbursement transactions @ $650 average per transaction
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- $2 BILLION in annual vendor disbursements
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- How many vendorsdoes your company really have?
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- Is IEE, Inc. the same vendor as I2E, Incorporated?
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- Inaccurate/sloppy data makes fraud easy to conceal
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- Vendor consolidationsaves money
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3. Economics of Fraud ABC Company Revenue - $10 million Profit margin 10% 4. Anomaly DetectionBenefits
- Identifiesfraud
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- Ghost vendors, ghost employees, employees as vendors, fraudulent invoices, etc.
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- Divine Security has identified fraud and duplicate payments in 100% ofanomaly detection engagements.
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- Uncover fraud before the costs reach damaging proportions.
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- Savesmoney
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- Identify vendor relationships
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- Larger discounts through volume purchases
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- Duplicate payments, overpayments, chargebacks, etc.
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- More than 90% of our clients have been able to cover our fees with just the duplicate payments we identified.
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- Identifies managementcontrol issues
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- Inactive suppliers, low value invoices, multiple accounts for supplier, incomplete/inaccurate records
5. Anomaly Detection Approach
- Normalize and analyzeinternal company data
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- Vendor; accounts payable
- Testit (using up to 100 Divine profiles) against
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- Selected internal data
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- Human resource; payroll
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- Selected external data
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- Social Security Number
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- Corporate Records
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- Dun & Bradstreet
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- Identify anomaliesthat may be indicative of fraud
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- Vendor phone number matches employee phone number
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- Vendor bank/account # matches employee bank/account #
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- Employee name matches vendor officer / director name
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- Employee SSN matches SSA paid death claim
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- 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
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- Ranked by anomaly risk score
- A summary of conflicts
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- Between data on different systems or from different sources
- Identification of primary fraud risks
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- 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
- E-mail: [email protected]