Generative AI for Fraud Detection and Regulatory Compliance

Financial fraud is evolving rapidly, demanding more sophisticated tools than traditional rule-based systems can provide. This course provides a foundational understanding of how generative AI enhances detection accuracy and automates complex reporting tasks in the financial sector. You will gain the skills to transform reactive compliance measures into proactive defense strategies using modern AI techniques. By the end of this course, you will be able to interpret AI outputs, refine detection prompts, and understand the integration of large language models into existing risk frameworks. What you'll learn: - Understand the core principles of generative AI and its specific utility in detecting financial anomalies - Apply prompt engineering techniques to analyze suspicious transaction patterns and generate risk summaries - Explore Retrieval-Augmented Generation (RAG) to cross-reference transactions against internal policies and global regulations - Practice using AI to generate synthetic data for testing detection systems without compromising sensitive information - Evaluate the ethical implications and bias risks associated with using AI in automated compliance decisions - Develop structured workflows for drafting automated Suspicious Activity Reports (SARs) through written exercises The curriculum begins with essential terminology and the mechanics of large language models before moving into practical text-based applications focused on real-world fraud scenarios. You will work through detailed explanations and code snippets that illustrate how to implement these technologies effectively. This course is designed for beginners in compliance, risk management, or data analysis who want to understand the future of financial security. No prior experience with AI or programming is required to begin. Start building your expertise in AI-driven financial security today.