Part 2 Building AI for credit risk
Credit risk sits at the heart of every financial institution’s stability. It’s also one of the most heavily regulated domains in finance, where a model that can’t explain its decisions is a model that can’t 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 life cycle and the domain-specific metrics and transformations, such as Kolmogorov-Smirnov (K-S) statistics, Weight of Evidence (WoE), and information value (IV), that remain indispensable even alongside modern machine learning (ML). 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.