INFOSEC 690F Fraud Detection

This course provides an introduction to the principles and practice of fraud detection across a variety of problem domains such as money laundering, credit card fraud, telecommunications fraud, and computer and network intrusion. The key topics of this course include defining fraud in various domains; the interactions between fraud prevention and fraud detection; data collection and management; statistical tests and statistical power; methods for statistical fraud detection, including numerical validation, outlier detection, supervised and unsupervised classification methods, and application of Benford's Law. 

• Introduction and Course Overview: What is fraud? Why does it happen? Who is responsible for preventing and detecting it?
• Fraud Prevention: Risks that lead to fraud. Controls to prevent fraud.
• Symptoms of Fraud: Outliers, anomolies, implausibilities, and impossibilities.
• Auditing: Overview of auditing. Auditing plans, goals, possible outcomes. Computer-assisted auditing techniques.
• Dealing with Data: Collecting data. Cleaning and normalizing data. Data attributes and structure. Data integrity.
• Understanding Data: Computer analysis and techniques. Sorting, indexing, summarizing, and stratifying. Duplicate detection. Joining data tables. Pivot tables and cross tabulation.
• Symptoms of Known Fraud: Payroll fraud. Overpayments. Ven- dor/employee interaction. Kickbacks. Bid rigging.
• Testing for Outliers: Statistical tests. Statistical power. Randomization testing.
• Modeling Behavior: Supervised and unsupervised learning techniques for fraud detection.
• Other Test for Fraud: Frequently used values. Even amounts and rounding. Ratio/variance analysis. Min/maxes. Benford's Law.
• Verifying Results and Improving Process: Sampling. Limits of computer- aided auditing. Preventative, detective, and corrective controls.