Post by sourceCode

Most AI credit scoring projects don't fail on accuracy. They fail when compliance asks: "Can you show me, in writing, why the model declined this specific applicant?" If the honest answer involves a data scientist opening a notebook and reverse-engineering a decision, the model isn't production-ready. Regardless of its AUC score. In Singapore and Hong Kong, this is no longer a theoretical concern. MAS FEAT and Project Veritas have moved the conversation from AI adoption to AI accountability. HKMA's four pillars require human oversight that's risk-calibrated, documented, and evidenced, not ad hoc. And the gap most institutions have isn't in the model. It's in the system surrounding it. In our latest article, we break down: - Why feature attribution (SHAP, LIME) is not explainability - The 5-layer architecture that actually passes regulatory review - What MAS and HKMA are likely to scrutinize next - The 5 questions every CTO should be able to answer before go-live The institutions that will lead the next phase of AI lending won't have the most accurate models. They'll have the most defensible decisions. #AICreditScoring #ExplainableAI #MAS #HKMA #BFSI #DigitalLending #APAC #sourceCode