Part 1: Foundations of financial AI
Building AI for finance is not the same as building AI for any other industry. The stakes are higher, the regulations are stricter, and the data is messier. Before you can build anything meaningful, you need to understand the terrain.
In chapter 1, you’ll learn why financial AI demands a domain-first approach and be introduced to the 4-Layer Framework—spanning Data Assets, Modeling, Strategy & Monitoring, and Application—that will serve as your architectural blueprint for every project in this book. Chapter 2 steps back from algorithms to examine the strategic realities that determine whether a model actually makes it to production: organizational buy-in, compliance hurdles, and the often-underestimated gap between a promising prototype and a system that reliably generates business value. Chapter 3 then explores generative AI and Large Language Models in the financial context, showing how these tools can augment workflows—from document analysis to agentic decision support—while operating within the strict guardrails that regulated industries demand.
When you’ve completed this part, you’ll have the conceptual foundation and the practical framework to make the hands-on projects in the rest of the book far more meaningful.