1. experience clarity // CPAs & ADVISORS FRAUD TRENDS AND DETECTION: AN UPDATE Shauna Woody-Coussens, CFE Managing Director – Forensic & Valuation Services

2. COST OF INSURANCE FRAUD Nearly $80 billion in fraudulent claims made in the US annually 1 May be a conservative figure as much insurance fraud goes undetected and unreported Fraudulent claims estimated to increase the average household’s insurance costs by more than $300 per year After narcotics trafficking, insurance fraud is the largest criminal enterprise in the US 2 Insurance fraud is the 2 nd most costly white-collar crime in US, behind tax evasion 3 1 Coalition Against Insurance Fraud estimate 2Property Casualty 360 3National Insurance Crime Bureau 2 // experience clarity

3. DATA ANALYTICS AND THE INSURANCE INDUSTRY Traditional and recent uses Actuarial risk analysis Behavior-based credit scores as an enhancement to personal auto insurance underwriting Predictive and optimization models in business processes such as sales, marketing and service 3 // experience clarity

4. MY GOAL TODAY… Convince you to use data analytics within your organization to help you prevent and detect occupational fraud 4 // experience clarity

5. OCCUPATIONAL FRAUD TRENDS 5 // experience clarity

6. ACFE 2014 REPORT TO THE NATIONS Typical organization loses 5% of its annual revenue to fraud Median loss was $145,000 for all companies One fifth of losses were over $1 million Frauds lasted 18 months before being detected 58% of victim organizations recover nothing 6 // experience clarity

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13. Answer questions through use of analytical software As simple as Excel Filter Sort As complex as you want to make it ACL IDEA SQL DATA ANALYTICS 13 // experience clarity

14. WHAT’S THE BIG DEAL? “Big Data” nightmare – we need help Manual review is inefficient Suspicious activity is a 96.5% match to normal, so you are less likely to notice it through a manual review Sampling does not reveal patterns & trends System weaknesses lead to fraud So… even if no fraud is evident, weaknesses are often uncovered that can be corrected to help mitigate fraud 14 // experience clarity

15. APPLICATIONS IN THE INSURANCE INDUSTRY 15 // experience clarity

16. Corruption Billing Expense reimbursement Non-cash Payroll TOP OCCUPATIONAL FRAUD SCHEMES IN THE INSURANCE INDUSTRY 16 // experience clarity

17. Corruption 17

18. CORRUPTION An employee misuses his or her influence in a business transaction in a way that violates his or her duty to the employer in order to gain a direct or indirect benefit In my experience, the most common form of corruption is the payment of kickbacks to related to purchases 18 // experience clarity

19. CORRUPTION EXAMPLE Insurance agent colluded with an auto glass vendor to bill for OEM glass replacement when wrecking yard glass often used Data mining pointed out unusual level of OEM glass pricing for that vendor Loss was $500,000 19 // experience clarity

20. RED FLAGS FOR CORRUPTION Off-book fraud, so very hard to detect Payments often do not go through the organization’s accounting records Payments often paid in cash Look for “behavioral” red flags Rapidly increasing purchases from one vendor Excessive purchases of goods and services Too close of a relationship with a vendor 20 // experience clarity

21. Compare order quantity to optimal reorder quantity Compare purchase volumes/prices from like vendors Compare quantities ordered and received Check for inferior goods (# of returns by vendor) Unstructured data review (read suspected fraudster’s email….) DATA ANALYTICS FOR CORRUPTION 21 // experience clarity

22. Billing Schemes 22

23. Fraudster creates false support for a fraudulent purchase, causing the nonprofit to pay for goods or services that are nonexistent, overpriced or unnecessary Invoicing via shell company Invoicing via an existing vendor False invoicing for non-accomplice vendors Pay-and-return schemes Personal purchases with nonprofit’s funds BILLING SCHEMES 23 // experience clarity

24. BILLING FRAUD EXAMPLE Employee set up fictitious vendors to bill employer for purchases never made Employee made repeated purchases in the amount of $24,950 when his sole authority was $25,000 Loss was $2.2 million over a 2 year period 24 // experience clarity

25. Vendor anomalies Payment anomalies Purchasing anomalies Accounts payable invoices Credit card/p-card purchases RED FLAGS FOR BILLING SCHEMES 25 // experience clarity

26. Vendor attribute analysis Trending of vendor activity Identification of “high risk” payments DATA ANALYTICS FOR BILLING SCHEMES 26 // experience clarity

27. Expense Reimbursements & Purchasing Cards 27

28. EXPENSE REIMBURSEMENT/P-CARDS Any scheme in which an employee makes a claim for reimbursement or fictitious or inflated business expenses Employee files fraudulent expense report, claiming personal travel, nonexistent meals, etc. Employee purchases personal items and submits and invoice to employer for payment Employee purchases goods/services for inappropriate uses and charges to employer for payment 28 // experience clarity

29. RED FLAGS FOR EXPENSE REIMBURSEMENT /P-CARD SCHEMES Expenses exceed what was budgeted or prior years totals Expenses claimed on days employee did not work Purchases that do not appear to be business related Minimal or non existent support for requests Altered receipts Unusual or excessive reimbursements to one employee Submitted receipts are consecutively numbered Expenses in round dollar amounts Expenses just below receipt submission threshold 29 // experience clarity

30. Identify transactions on weekends, holidays or while employee is on vacation Identify split transactions in which a large purchase are split into smaller transactions just under approval threshold Identify unusually high or frequent expense reimbursement/p-card usage Identify expenses in round dollar amounts DATA ANALYTICS FOR EXPENSE REIMBURSEMENT/P- CARD SCHEMES 30 // experience clarity

31. Non-Cash 31

32. NON-CASH FRAUD SCHEMES Any scheme in which an employee steals or misuses non-cash assets of the victim organization Employee steal inventory from a warehouse or storeroom Employee extracts customer’s personal and account information from a database and then sells that data (identity theft) Employee steals employer’s competitive data and supplies it to a competitor Common when employees change employers 32 // experience clarity

33. RED FLAGS FOR NON-CASH SCHEMES Shrinkage in inventory/supplies Employees who frequently visit the office after hours Missing/borrowed tools, equipment, office supplies, etc. Missing, altered, or unmatched supporting documents 33 // experience clarity

34. DATA ANALYTICS FOR NON-CASH SCHEMES Automated monitoring of: Online transactions and inquiries The date, time and source of online access, especially if the system can be accessed from a WAN or the Internet Report generation and downloading, including operational and custom reports or queries, especially those containing customer/client account information Emails sent and received and attachment sizes 34 // experience clarity

35. Payroll 35

36. Ghost employees Fictitious employees entered into payroll system Terminated employees Terminated employees remain on payroll system with direct deposit to a current employee’s account Duplicate payroll Overpayment schemes Higher pay rates, inflated hours, unauthorized bonuses PAYROLL SCHEMES 36 // experience clarity

37. PAYROLL FRAUD EXAMPLE Payroll manager got the technical support staff at the payroll service provider to alter programming in her desktop software Generated altered payroll reports from her desktop to hide the theft and used the altered reports to record to the ledger Loss was $350,000 over four years 37 // experience clarity

38. Look for lack of: Bank accounts for electronic payments Home addresses and phone numbers Holiday leave, vacation or sick leave Benefit/tax deductions Also look for Duplicate SSNs Duplicate bank account numbers Duplicate home addresses PO box addresses Payments after termination RED FLAGS/DATA ANALYTICS FOR PAYROLL SCHEMES 38 // experience clarity

39. INNOVATIONS IN ADVANCED TECHNOLOGY TOOLS 39 // experience clarity

40. TYPES OF UNSTRUCTURED DATA Email (corporate and personal) Network Files and ECM Systems Phone records, cell phones Computer hard drives (deleted activity) Internet history, social media, chat, Skype, IM Board minutes, performance appraisals 40 // experience clarity

41. LEVERAGE DATA IN YOUR ORGANIZATION 41 // experience clarity

42. DATA ANALYTICS – A GUIDE TO APPLICATION 1.Build a profile of potential risks What are your highest risk business processes? What frauds could occur in those processes? What would red flags for fraud look like in those business processes? 2.Identify data available to help test for potential fraud Identify and define specific fraud risks to be tested For each risk, identify and define data requirements, data access processes and analysis logic 3.Develop procedures & analyze data Start with relatively simple tests and then add more complex analysis building a library of specific tests This is not testing a sample, it is testing the POPULATION 42 // experience clarity

43. DATA ANALYTICS – A GUIDE TO APPLICATION 4.Make analysis results understandable Try to answer one question at a time 5.Does analysis result address the identified fraud risk? If not, go back to step #3 and refine Are there additional tests that are needed 6.Perform investigation of anomalies or unexpected patterns, as appropriate 43 // experience clarity

44. QUESTIONS? Contact Information Shauna Woody-Coussens, CFE BKD, LLP 1201 Walnut, Ste. 1700 Kansas City, MO 64106 816-701-0250 swoodycoussens@bkd.com 44 // experience clarity

45. THANK YOU FOR MORE INFORMATION // For a complete list of our offices and subsidiaries, visit bkd.com or contact: Shauna Woody-Coussens, CFE // Managing Director swoodycoussensl@bkd.com // 816.701.0150 45 // experience clarity