Predictive Analytics for Financial Risk Assessment: A Machine Learning Framework

Financial institutions face increasing complexity in assessing borrower default, market risk and operational risk. This paper proposes a unified machine-learning framework that combines gradient-boosted tree ensembles (XGBoost, LightGBM), a stacked meta-learner, and a SHAP-based interpretability layer to produce risk scores compliant with IFRS 9 expected-credit-loss requirements. We evaluate the framework on a corpus of 1.2 million loan records and observe a 17.4% improvement in AUC over a logistic-regression baseline, while preserving auditable per-feature attributions. We further show that calibrated probability outputs reduce regulatory capital requirements by an estimated 8.6% versus conservative IRB models, and discuss deployment considerations including concept-drift monitoring, fairness audits, and integration with bank treasury workflows. The contribution is both methodological — a generalised stacking architecture for risk applications — and empirical, providing a benchmark on a large real-world portfolio.