In this five part master series, class participants will gain a comprehensive view into the world of forensic data analytics (FDA). From understanding practical use cases of FDA in the audit, internal audit, investigations and proactive monitoring to understanding necessary data sources, validation proceudres, success factors and pitfalls to avoid. We will also explore hundreds of leading anti-fraud tests around corruption, conflicts of interest, fake vendor schemes, segregation of duties, revenue recognition and sales abuse, among dozens of other schemes. Participants will develop their own library of anti-fraud tests from various open source locations and apply them in relevant contexts such as the COSO Fraud Risk Management Guide. Finally, participants will also peek into future innovations in FDA and how technologies such as ChatGPT, data visualization, machine learning, AI and cryptocurrencies are all impacting the discipline. We can't promise you'll be a programmer after this course, but you will certainly be armed with the information needed to project manage an FDA project and "direct" the programmers to accomplish your fraud risk / FDA objectives.
Learning Objectives
- Understand Forensic Data Analytics and practice use cases
- Learn fundamentals of data types, data extraction, tranformation and validation techniques
- Understand five key applications of FDA in line with COSO's Fraud Risk Management Guide
- Build your own library of FDA tests and know when and how to apply them
- Learn emerging innovations in FDA and how Generative AI, ChatGPT, Machine Learning, Data Sharing Consortiums and Cryptocurrencies all play a role
Major Topics
- Forensic data analytics
- Fraud prevention and detection
- Data validation and methodologies
- COSO's Fraud Risk Management Guide principles
- Innovations in anti-fraud technology
