FRAUD Prevention & Detection. Group Members Raven Smith Tommy Harville Kedron Hilario

FRAUDPrevention & Detection

Group Members

• Raven Smith

• Tommy Harville

• Kedron Hilario

What is Fraud?

• Surprise• Trickery• Cunning• Unfair ways by

which another is cheated

Fraud is not accidental

• Fraud is done with intent to deceive

• Financial mistakes that occur by accident are not considered fraud

Types of Fraud

• Employee Embezzlement

• Vendor Fraud

• Customer Fraud

• Management Fraud

• Investment Scams

• Consumer Fraud

Employee Embezzlement

• Most Common

• Stolen Cash or inventory

Vendor Fraud

• Vendor overbills

• Fewer goods are provided

• Collaboration

• Dummy addresses & fictitious vendors

Customer Fraud

• Bills aren’t paid

• Pays less than the bill

• Counterfeit money

• Receive assets they do not deserve

• Theft

• Lies

Management Fraud

• Most expensive

• Managers/Top Executives

• Financial Statement manipulation

• Motif – higher compensation

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

Consumer Frauds

• Telemarketing

• Identity Theft

• Internet Fraud

Who Commits Fraud?

Why Frauds Occur?

Fraud Prevention

Fraud Prevention

Creating A Control Environment

• Tone at the top

• Code of conduct/ethics

• Anti-fraud policies

• Whistleblower hotlines

Sharing Information & Communication

• Classroom-style fraud awareness training

• Consistent reinforcement

• Orientation of new hires

• Annual performance evaluations

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

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

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

Fraud Detection

Fraud Detection

• 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

Fraud Detection

• Who’s Responsible?

– Auditors

– Management

– Financial Staff

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

Fraud Detection

• Data mining fraud detection techniques

– Efficient

– Specific in use

– Vary in accuracy and price

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

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

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

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

Fraud Detection

• Genetic Algorithms– Commonly used to detect searching

problems

– Often combined with other data mining techniques to decrease the sensitivity of alerts

Class Discussion

• Are there any other fraud prevention tactics you would suggest?

• Are there any other fraud detection techniques you would suggest?

Conclusion

• What is fraud?

• Fraud Prevention

• Fraud Detection

In the famous words of Porky Pig…