The document discusses vendor anomaly detection and fraud prevention. It notes that complex business structures make fraud difficult to detect, with the median loss from accounts payable fraud being $250,000. Anomaly detection can identify fraud like ghost vendors, duplicate payments, and fraudulent invoices by normalizing and analyzing a company's internal vendor and payment data and comparing it to external sources to find anomalies that may indicate fraud. This approach can uncover fraud before costs become too large and help identify management control issues.

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 makes fraud detection difficult. Median loss due to accounts payable fraud is $250,000 Source: Association of Certified Fraud Examiners How can fraud be found in massive amounts of data? One company’s A/P profile 30,000 vendors Three million annual disbursement transactions @ $650 average per transaction $2 BILLION in annual vendor disbursements How many vendors does your company really have? Is “IEE, Inc.” the same vendor as “I2E, Incorporated”? Inaccurate/sloppy data makes fraud easy to conceal Vendor consolidation saves money

3. Economics of Fraud ABC Company Revenue - $10 million Profit margin – 10%

4. Anomaly Detection – Benefits Identifies fraud Ghost vendors, ghost employees, employees as vendors, fraudulent invoices, etc. Divine Security has identified fraud and duplicate payments in 100% of anomaly detection engagements. Uncover fraud before the costs reach damaging proportions. Saves money 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 management control issues Inactive suppliers, low value invoices, multiple accounts for supplier, incomplete/inaccurate records

5. Anomaly Detection – Approach Normalize and analyze internal company data Vendor; accounts payable Test it (using up to 100 Divine profiles) against Selected internal data Human resource; payroll Selected external data Social Security Number Corporate Records Dun & Bradstreet Identify anomalies that 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. Benford’s 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: todd.petty@divine.com