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This course, GenAI for Fraud Analysts: Improving Detection Efficiency, empowers fraud analysts and compliance professionals with cutting-edge Generative AI (GenAI) techniques to stay ahead of sophisticated fraud schemes.
Through hands-on learning and real-world applications, you will master AI-driven anomaly detection, synthetic data generation, and automated compliance checks. Using tools like Python, Scikit-learn, ChatGPT, and Faker, you'll develop fraud detection pipelines, visualize actionable insights, and enhance risk management strategies. This course is designed for beginner fraud analysts, compliance officers, financial professionals, IT security specialists, and data analysts who want to enhance their fraud detection and compliance strategies using Generative AI (GenAI). It is also ideal for aspiring professionals looking to explore AI-driven anomaly detection, risk management, and fraud prevention. Whether you're aiming to improve detection accuracy or automate compliance workflows, this course will equip you with the skills to stay ahead of evolving fraud tactics. To get the most out of this course, learners should have a basic understanding of fraud detection concepts such as transaction monitoring, anomaly detection, KYC (Know Your Customer), and AML (Anti-Money Laundering). Familiarity with data analysis tools like Excel and visualization software is recommended, along with a foundational knowledge of Generative AI tools such as ChatGPT and Python. No advanced programming experience is required, but a willingness to explore AI applications in fraud prevention will be beneficial. By the end of this course, learners will be able to analyze the role of Generative AI in fraud detection and compliance and effectively integrate it into existing workflows. They will gain hands-on experience in applying AI-powered tools like ChatGPT and Python to identify anomalies, generate synthetic data, and enhance fraud detection accuracy. Additionally, participants will learn to design AI-driven workflows to automate compliance tasks, improve operational efficiency, and evaluate fraud detection patterns to make more informed, proactive decisions in combating fraud.
