*8.0* CPEs: (2-1/2 Day Seminars) Data Analytics, CHAT GPT & AI" with Mark Nigrini

Financial transactions are increasingly digital, and payments processing is increasingly automated, which makes our role as fraud examiners more important than ever. Now is the time to delve into the potential of AI platforms such as ChatGPT to raise our workplace productivity, assist our fraud examination workflows and speed up or automate routine tasks, allowing us to keep our focus on strategic fraud prevention and detection activities. By demonstrating AI’s capabilities in natural language processing and generation, we will show how AI-based tools can streamline communications, answer queries and help with interactions within your organization. Additionally, AI can even help us find the formula(s) that will return the desired results in Excel or any other app. We will also look at the darker side of AI to answer questions such as: What can ChatGPT and other tools learn from your data and queries and explicitly or implicitly pass on to the rest of its userbase? Or, if you repeatedly ask AI to identify the purchase of gift cards by employees using P-cards, will the AI tool “learn” that this is a recurring and significant problem in your company?

You Will Learn How To:

• Identify the potential positive impact of AI on productivity, efficiency and fraud detection.
• Include the use of AI in the planning and reporting phases of an investigation.
• Solve hypothetical and real-world problems with the help of AI.
• Appraise the confidentiality-related issues related to sharing data and information with the tool.

OUR SPEAKER

Mark Nigrini, CA(SA)
Professor, West Virginia University

Mark J. Nigrini is an associate professor of accounting at West Virginia University. His research passion for many years has been a phenomenon known as Benford’s Law which is related to the patterns of the digits in tabulated data. The smaller digits (1s, 2s, and 3s) are expected to occur more often in scientific and financial data and Benford’s Law has shown itself to be valuable to fraud examiners in their quest to uncover fraud in corporate data. His current research addresses insider threats, and more specifically the cybersecurity risks posed by former employees. 

Nigrini is the author of Forensic Analytics (Wiley, 2020) which describes analytic tests used to detect fraud, errors, estimates, and biases in financial data, and Benford's Law (Wiley, 2012). In 2014 he published an article in the Journal of Accountancy that was co-authored with Nathan Mueller, a fraudster. That article won the Lawler award for the best article in the Journal of Accountancy in 2014. Included in his array of academic publications is the lead article in the new premier forensic accounting journal, the Journal of Forensic Accounting Research. His recent publications include the lead article in the Nov/Dec 2020 issue of Fraud Magazine. His work has been featured in, amongst others, The Financial Times, New York Times, and The Wall Street Journal. His radio interviews have included the BBC in London, and NPR in the United States. His television interviews have included an interview on a fraud saga for the Evil Twins series for the Investigation Discovery Channel. He is a regular presenter at the ACFE’s Global Conferences and at events overseas such as Brazil’s Interforensics 2023.