1. FRAUD Prevention & Detection

2. Group Members Raven Smith Tommy Harville Kedron Hilario

3. What is Fraud? Surprise Trickery Cunning Unfair ways by which another is cheated

4. Fraud is not accidental Fraud is done with intent to deceive Financial mistakes that occur by accident are not considered fraud

5. Types of Fraud Employee Embezzlement Vendor Fraud Customer Fraud Management Fraud Investment Scams Consumer Fraud

6. Employee Embezzlement Most Common Stolen Cash or inventory

7. Vendor Fraud Vendor overbills Fewer goods are provided Collaboration Dummy addresses & fictitious vendors

8. Customer Fraud Bills aren’t paid Pays less than the bill Counterfeit money Receive assets they do not deserve Theft Lies

9. Management Fraud Most expensive Managers/Top Executives Financial Statement manipulation Motif – higher compensation

10. Investment Scams A Ponzi scheme is a fraudulent investment operation where the operator, an individual or organization, pays returns to its investors from new capital paid to the operators by new investors, rather than from profit earned by the operator

11. Consumer Frauds Telemarketing Identity Theft Internet Fraud

12. Who Commits Fraud?

13. Why Frauds Occur?

14. Fraud Prevention

16. Creating A Control Environment Tone at the top Code of conduct/ethics Anti-fraud policies Whistleblower hotlines

17. Sharing Information & Communication Classroom-style fraud awareness training Consistent reinforcement Orientation of new hires Annual performance evaluations

18. Designing & Implementing Antifraud Control Activities Establishment and maintenance of internal controls Segregation of duties Avoidance/discouragement of related party transactions Providing Board of Directors with oversight of management Use of encryption and complex passwords

19. Monitoring Activities Monitoring effectiveness of antifraud policies and controls Regular enforcement of policies and procedures –Creates a motivation within employees to comply –Worse to have a written policy that isn’t enforced than to not have a policy at all

20. Performing Audit Risk Assessments Giving internal auditors investigation authority –Examine and evaluate internal controls –Determine effectiveness of management’s actions –Evaluate control environment, key indicators of fraud, identify internal control weaknesses, recommend investigations –Perform fraud risk assessment

22. Fraud Detection

23. Who’s Responsible? Fraud Detection from and Auditor’s Perspective Fraud Detection using Data Analytics –Neural Networks –Decision Tree –Hidden Markov Models –Artificial Immune Systems –Genetic Algorithm

24. Fraud Detection Who’s Responsible? –Auditors –Management –Financial Staff

25. Fraud Detection Frequent evaluations of internal controls by auditors are suggested Auditors look for stressed relationships and lack of compliance with audit requests as a potential sign of fraudulent activity Companies with strong internal controls are tested more frequently on transactions. Testing on transactions has proven to be a very effective fraud detection technique used by auditors

26. Fraud Detection Data mining fraud detection techniques –Efficient –Specific in use –Vary in accuracy and price

27. Fraud Detection Neural Networks –Banks are considered to be the largest user –Ability to learn from previous instances Supervised training – training with fraudulent and non- fraudulent data in the same set. Unsupervised training – training on only one type of data –The ability to detect fraud comes from pattern recognition developed by training techniques

28. Fraud Detection Decision Trees –A type of network that uses a set of rules to break down one complex issue into many, manageable problems. –Can flag multiple transactions as fraud in a given instance by grouping similar transactions –Has the ability to learn and recognize patterns –Very flexible and operate quickly

30. Fraud Detection Hidden Markov Model –Depends on a profile set up to recognize spending habits –Profile logs amount spent, vendor, city, time of purchase etc. –Uses 3 categories to classify transactions High Profile Medium Profile Low Profile –Excellent at detecting fraud quickly, but also can overreact to changes in spending habits

31. Fraud Detection Artificial Immune Systems –Typically used to prevent intrusions and detect viruses –Only needs non-fraudulent data to train on to recognize fraudulent transactions –Adaptive capabilities –Favorable technique due to its relatively low cost, accuracy, and speed

32. Fraud Detection Genetic Algorithms –Commonly used to detect searching problems –Often combined with other data mining techniques to decrease the sensitivity of alerts

33. Class Discussion Are there any other fraud prevention tactics you would suggest? Are there any other fraud detection techniques you would suggest?

34. Conclusion What is fraud? Fraud Prevention Fraud Detection

35. In the famous words of Porky Pig…