Machine Learning for Finance. Build Next-Gen AI Systems for Trading, Forecasting, Risk, and Compliance Ahmed Menshawy, Mahmoud Fahmy

The integration of machine learning into finance is driving a fundamental shift—optimizing risk, forecasting markets, and enabling smarter compliance. Machine Learning for Finance is a hands-on, end-to-end guide designed to help readers bridge the gap between AI research and real-world financial deployment. Written by two leading AI engineers at Mastercard, this book combines deep technical insight with practical finance-specific workflows. Starting with the foundations of ML, GenAI, and financial data, the book builds toward sophisticated applications such as agentic AI, reinforcement learning, and MLOps for finance. You'll learn to generate synthetic data using GANs, extract insights from SEC filings using transformers, and deploy real-time trading bots with monitoring pipelines. Each chapter includes projects modeled after real use cases—fraud detection, risk modeling, portfolio optimization, and more. Machine Learning for Finance is tailored for practitioners. It goes beyond code snippets and theory, offering full-stack implementations that scale in production environments. Whether you're automating regulatory filings or building robo-advisors, this book gives you the tools to deliver AI innovation in one of the most complex, regulated industries.