Detecting & Preventing Procurement Fraud Using Data Analysis to Detect Improper Disbursements

Transcription

1 Detecting & Preventing Procurement Fraud Using Data Analysis to Detect Improper Disbursements April 29, :00 3:00pm ET Andrew Simpson, MBA Chief Operating Officer, CaseWare Analytics Paul Soos, CFE, CIA, CRMA, CICA Manager, Anti-Fraud Services CBIZ Risk & Advisory Services, LLC Agenda Welcome & Introductions Understanding Fraud Data Analysis Proactive Procurement Anti-Fraud Reviews Easy to Run Data Analysis Tests Summary Q&A 1

2 Understanding Fraud The Fraud Triangle Donald Cressey Incentive Opportunity Rationalization Understanding Fraud The Three Main Types of Fraud (2012 RTTN) Fraudulent Financial Statements 7.6% - $1M Concealed liabilities, fictitious revenues, improper valuation Corruption Schemes 33.4% - $250K Conflicts of interest, bribery, improper gratuities Asset Misappropriation 86.7% - $120K Stealing stuff $ (88%), Inventory, Other Assets Billing schemes, T&E, check tampering 2

3 Fraud in the Real World Source: 2012 ACFE Global Fraud Study Duration of Fraud The average time taken to find fraud is 18 months. Source: 2012 ACFE Global Fraud Study 3

4 Poll Question 1 What is your organization s approach to managing the risk of fraud? A. React to events as they occur B. Proactively addressing risks C. Using a Strategic Risk-based Method D. Other Data Analysis: Industry Standard Able to analyze entire data populations Makes data imports easy to accomplish Preserves data integrity Allows for accessing, joining, relating, and comparing data from multiple sources Requires minimum IT support for data access or analysis to ensure auditor independence 4

5 Data Analysis: Process Identify the processes or areas with the highest risk of fraud in the business Select a high risk process or area and identify how fraud could occur: How can someone perpetuate fraud? How can fraud be concealed? Determine what the fraud would look like in the data Decide on the data needed and analyses to perform Data Acquisition Data Analysis: People Meet with the IT organization to align on the right data Look at the live data before exporting it for quality Data Analysis Workshop the first few projects to build capability Be expansive in your thinking Scripting At the end of every project decide if any analysis could or should be performed at a later time 5

6 Data Analysis: Technology Able to analyze entire data populations Makes data imports easy to accomplish Preserves data integrity Allows for accessing, joining, relating, and comparing data from multiple sources Enhances fraud detection capabilities Audit trail & automation Poll Question 2 What is your biggest challenge in implementing an effective fraud management process? A. Human Resources B. No Strategic Approach C. Technology D. All of the Above 6

7 Proactive Procurement Anti-Fraud Reviews Accounts Payable/Human Resources Testing Vendor Master File (incomplete records, shared addresses, TIN, phone) Invoice Testing (even dollar, sequential, numbering) Employee Testing (SSN, shared addresses, bank accounts) Shell company (vendors and employees sharing info addresses, bank accounts Proactive Procurement Anti-Fraud Reviews A/P Analysis Shared Vendor Addresses Fraud Risk: Shell Companies 7

8 Summarize Invoice File Summarize Invoice File 8

9 Join Vendor Master to Invoice Data Join Vendor Master to Invoice Data 9

10 Append Address Number Field Append Address Number Field 10

11 Duplicate Key Detection Search Duplicate Key Detection Search 11

12 Duplicate Key Detection Search Some manual review is needed for false positives Duplicate Key Detection Search Check for related companies 12

13 Duplicate Key Detection Search Potential related companies Duplicate Key Exclusion Search To extract out same name vendors 13

14 Duplicate Key Exclusion Search To extract out same name vendors Duplicate Key Exclusion Search Final results before manual review for false positives 14

15 Proactive Procurement Anti-Fraud Reviews Purchase/Procurement Card (P-Card) Transactional/monthly/credit limit Potential split transactions Prohibited categories High-risk merchants (PayPal) Other policy violations Proactive Procurement Anti-Fraud Reviews P-Card Analytical Test Split Transactions Fraud Risk: Circumvention of Company Policy 15

16 Transaction Summarization Summarize transactions by Card #, Date, Merchant Summarization Extraction Extract Multiple Daily Merchant Transactions 16

17 Summarization Extraction Extract Multiple Daily Merchant Transactions Summarization Extraction 14 Results, 36 transactions Some obviously not splits to avoid limit 17

18 Summarization Extraction Extract Transactions Within Split Parameters Same Card Summarization Extraction 3 Results, 8 transactions Potential split summary, same Card 18

19 Transaction Summarization Summarize transactions by Date and Merchant (not Card #) Summarization Extraction Extract transactions within split parameters any card 19

20 7 dates/merchants, 16 transactions Summarization Extraction Results Potential split summary, any card Summarization Join Join summarization to transaction detail 20

21 Summarization Join Join summarization to transaction detail Summarization Extraction Detail of 16 potential split transactions 21

22 Other Proactive Anti-Fraud Reviews Travel & Entertainment (T&E) Policy compliance (company card, agency, etc.) Prohibited categories High-risk merchants (airfare) Journal Entry Testing Journal entries done by infrequent users Seldom used accounts Key word analysis (i.e. plug, error, round ) Accrual and revenue account entry analysis Poll Question 3 Which area in your organization has the highest rate of recurring issues? A. Accounts Payable/Purchasing Cards B. General Ledger C. Travel & Entertainment D. General Ledger 22

23 Proactive Procurement Anti-Fraud Reviews T&E versus P-Card Analytical Test Matching Transaction Date and Amount Fraud Risk: Improper/Duplicate Reimbursement File Join Join two files using transaction date and amount 23

24 File Join Join two files using transaction date and amount File Join Results 1 matched transaction for $1,

25 Poll Question 4 How often does your organization utilize data analysis in your reviews? A. Frequently B. As needed C. Rarely D. Never Summary Identify areas with the highest risk of fraud in the business or those with recurring issues Look at the live data before exporting it for quality Workshop the first few projects to build capability Technologies are available with audit specific capabilities 25

26 Questions Additional Resources Whitepaper: Active Fight against Fraud 50 Packaged Analytics: SmartAnalyzer Andrew Simpson, MBA COO, CaseWare Analytics Paul Soos, CFE, CIA, CRMA, CICA Manager, Anti-Fraud Services CBIZ Risk & Advisory Services, LLC 26