Our new book is out for preorder! We have been working hard to get this book out, and we are thrilled to finally share it with you. While the book comes out in December 2025, you can already go through the labs! Find them here. A preliminary Table of Contents, Figures and Algorithms can be found here.

The book summarizes close to a decade of experience in applying deep learning to banking problems, with a focus on risk management. We are proud to have written a book structured for both academic and professional use. Our target is data scientists developing models, risk managers evaluating them, regulators shaping policy, and graduate students preparing for a career in financial technology. We assume you have a background in data science, but we guide you through the specific nuances of applying deep learning in the high-stakes environment of banking.

What’s Inside?

We wanted to create a single resource that bridges the gap between cutting-edge theory and the practical realities of banking. The book provides:

• A Comprehensive Toolkit: Dive deep into the essential deep learning architectures—from Convolutional Neural Networks (CNNs) for image analysis to Transformers for text and Graph Neural Networks (GNNs) for understanding financial contagion.
• Real-World Case Studies: Each chapter is grounded in practical applications, using real data to demonstrate how these models can be applied to solve core banking challenges like mortgage default prediction, behavioral scoring, and risk analysis. We are proud to have received special permission from Freddie Mac to use their Single Family Loan Dataset for our book. We also use Fed speeches, network data, LiDAR, and a long list of alternative data for the book. All case studies are done on real data, with direct application to banking practice.
• Focus on Trust and Compliance: The book dedicates significant attention to the critical themes of fairness, accountability, explainability, and the ever-evolving regulatory landscape, including frameworks like the EU AI Act and regulators worldwide.
• Hands-On Learning: Every case study is accompanied by language-agnostic algorithms in the book, so it will never go out of fashion, and always up-to-date labs are available on the book’s companion website. This allows you to not just read about the models but to build and experiment with them yourself. You can try these right now!

Our goal was to create the book we wished we had: a single, unified resource that covers the data, the algorithms, and the business environment of AI in financial services. We believe that responsible AI can drive innovation and growth in banking, and this book is designed to give you the tools to build solutions that are safe, profitable, and productive. Interested? Preorder it now!