AI in Fraud & Risk Detection
Price range: $385.00 through $785.00
Overview
Date recorded: July 23, 2026
Presented by: Lanny Morrow, Director, Forvis Mazars LLP
Artificial intelligence is influencing how organizations approach internal fraud, waste, and abuse detection. Many leaders, however, are still building clarity around where these technologies fit, how they are intended to create value, and where professional judgment continues to play a central role.
This session offers a business-focused overview of AI technologies commonly applied in fraud and risk contexts, including machine learning, anomaly detection, generative AI, and automation. The discussion is designed to support a clearer understanding of how these tools can be used alongside existing analytics and investigative practices.
Using practical fraud and risk scenarios, the session explores how AI can help surface patterns, highlight unusual activity, and support more efficient review of large financial and operational data sets. The discussion also includes an illustration of how tools such as Microsoft Copilot can support early-stage analysis using structured or synthetic data, aligned with defined governance frameworks and usage guardrails.
Here’s What You Will Learn
- Gain clarity on the distinctions between artificial intelligence, machine learning, deep learning, generative AI, and agentic tools, and how each may relate to fraud and risk considerations.
- Explore how AI techniques can be applied to surface anomalies, unusual behaviors, and potential fraud indicators across varied scenarios.
- Develop an understanding of common AI and machine learning approaches used in fraud-related analysis, including supervised and unsupervised methods.
- Consider where generative AI may support efficiency in exploratory analysis, investigation scoping, and initial review, as well as where practical limitations remain.
- Recognize key considerations associated with AI-enabled fraud detection, including data quality challenges, false-positive considerations, evolving risk patterns, and regulatory or ethical factors.
- Examine practical ways to align rules-based analytics, AI, automation, and human insight into a more connected fraud detection approach.
- Review a Copilot-based example illustrating how AI can support initial fraud-related analysis and assist in generating insights from structured data.
Who Should Listen
Financial services leaders, including compliance officers, BSA/AML officers, risk leaders, and internal audit professionals seeking to enhance their approach to fraud detection through the use of AI-enabled techniques.
Program Level: Intermediate
Prerequisites: Basic Knowledge of AI and Fraud
Advanced Preparation: None
Delivery Method: Group Internet Based
Field of Study: Specialized Knowledge
Copyright Disclaimer
Any recording, transmission, retransmission, or republishing of any portion of this webinar is prohibited.
