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Financial AI in Practice: A playbook for credit, fraud, and investment systems https://WebToolTip.com English | 2026 | ISBN: 1633435393 (ISBN-13: 978-1633435391) | 464 pages | Epub | 17 MB In this important book, author Taehun Kim distills a decade of fintech experience into a repeatable 4-Layer Framework—Data Assets, Modeling, Strategy & Monitoring, and Application. As you go, you’ll build AI-first financial systems that take on four real-world challenges—credit scoring, fraud detection, investment, and personalized predictions. You’ll start with an end-to-end credit scoring pipeline that trains and deploys an XGBoost model to prepare and validate data, orchestrate recurring workflows, engineer features with Weight of Evidence and Information Value, and deliver verifiable explanations. You’ll also take on fraud detection using a tuned Light-GBM classifier with autoencoders that flag anomalous patterns through reconstruction error and a Graph Neural Network in PyTorch that uncovers fraud patterns in transaction networks. For investment, you’ll build a hybrid strategy that pairs a Random Forest engine with an LLM-powered news analyst. You’ll construct a full RAG pipeline that ingests the exact data sources you need and prompts a structured “LLM Analyst” portfolio advisor with a complete decision log.