Part 2: Building AI for credit risk
Credit risk sits at the heart of every financial institution’s stability. It is also one of the most heavily regulated domains in finance, where a model that cannot explain its decisions is a model that cannot be deployed. This part takes you from the fundamental question—“Can this borrower be trusted to repay?”—all the way to a production-ready, explainable credit scoring system.
Chapter 4 grounds you in the credit lifecycle and the domain-specific metrics and transformations—such as KS statistics, Weight of Evidence, and Information Value—that remain indispensable even alongside modern machine learning. In chapter 5, you shift from theory to practice, building an end-to-end pipeline that ingests raw financial data, orchestrates daily tasks with Airflow, trains an XGBoost model, and converts raw probabilities into an industry-standard credit score. Chapter 6 then elevates this workflow with automated binning, continuous drift monitoring, champion–challenger deployment strategies, and a robust explainability framework using SHAP and LIME—culminating in a look at how generative AI can bridge the gap between mathematical transparency and genuine human comprehension.
By the end of this part, you’ll have built a fully auditable credit model that satisfies both the rigor of data science and the scrutiny of financial regulators.