Generative AI for Fraud Detection Analytics

Financial fraud is evolving rapidly, and traditional rule-based systems are often insufficient to keep up with sophisticated modern threats. This course provides a clear path for understanding how generative AI can be used to identify suspicious patterns, automate risk assessment, and stay ahead of bad actors. You will gain the skills needed to integrate generative models into a fraud prevention workflow, focusing on data privacy and analytical accuracy. By the end of this course, you will be able to explain how these technologies transform raw transaction data into actionable security insights. What you'll learn: - Understand the core concepts of generative AI and its specific role in modern fraud analytics - Learn to generate high-quality synthetic datasets to train robust detection models without compromising sensitive information - Apply prompt engineering techniques to analyze transaction logs and generate detailed risk reports - Explore how Retrieval-Augmented Generation (RAG) can assist in cross-referencing activities against complex regulatory frameworks - Identify common fraud typologies and how generative models can help predict emerging threats - Practice evaluating model outputs for accuracy, bias, and explainability in a financial context The course begins with essential terminology and the evolution of fraud detection before moving into practical text-based modules on model implementation and risk assessment. You will progress from foundational theory to understanding how to apply these tools in real-world scenarios. This course is designed for beginners, aspiring data analysts, and security professionals who want to understand the intersection of AI and fraud prevention with no prior technical expertise required. Start building your expertise in the future of fraud prevention today.