Explainable Machine Learning Framework for Financial Risk Prediction and Decision Support Systems
Keywords:
explainable machine learning; financial risk prediction; decision support systems; algorithmic fairness; model governance; socio-technical infrastructure; regulatory complianceAbstract
The increasing reliance on machine learning models for financial risk prediction and decision support has raised critical concerns about transparency, accountability, and regulatory compliance. Black-box models, while achieving high predictive accuracy, often obscure the reasoning behind their outputs, making them unsuitable for high-stakes financial environments where explainability is both a regulatory requirement and a practical necessity. This paper proposes an explainable machine learning framework that integrates post-hoc interpretability techniques with inherently interpretable models to deliver both predictive performance and human-understandable explanations. The framework is designed as a modular socio-technical architecture, encompassing data preprocessing, model training, explanation generation, and decision support interfaces. Emphasis is placed on structural trade-offs between accuracy and interpretability, the governance of model deployment, and the sustainability of explanation pipelines under evolving financial regulations. The paper also examines fairness implications, showing how explanation mechanisms can expose biases in lending, credit scoring, and risk assessment. Policy recommendations are offered for integrating explainability into regulatory sandboxes and audit frameworks. By balancing technical rigor with institutional constraints, the proposed framework aims to serve as a blueprint for responsible AI in finance.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
