With regulators demanding transparency, explainable AI (XAI) is crucial in finance. While machine learning excels at detecting fraud or predicting credit risk, opaque models raise concerns. XAI techniques like model-agnostic explanations and feature attribution help both users and regulators understand why an algorithm made a decision.
This post explores finance‑industry case studies—from loan approval algorithms to portfolio management tools—and how banks deploy XAI for compliance and trust. It also covers the tension between model performance and explainability, and emerging standards. In highly regulated sectors, explainable AI is not just nice to have—it’s essential.
