Explainable AI for Credit Risk Assessment in Banking
Keywords:
Credit Risk Scoring Systems, Explainable Artificial Intelligence, Explainable Credit Assessment, Regulatory Compliance In Banking, Model Interpretability, Customer-Facing Credit Decisions, Consumer Protection Laws, Risk-Sensitive Capital Requirements, XAI Model Taxonomy, Post-Hoc Explanation Techniques, Credit Scoring Evaluation Frameworks, Model Transparency, Operational Explainability Metrics, Cognitive Evaluation Measures, Compliance-Oriented Explainability Tests, Model Monitoring And Validation, XAI Governance Frameworks, Banking Software Lifecycle Management, Risk Management Transparency, Interpretable Financial Models.Abstract
Credit risk scoring systems are among the most consequential decision-making models used by banks, and regulatory requirements around risk-sensitive capital calculations have made them mandatory in many jurisdictions. Beyond compliance, however, growing legal and commercial pressures are increasing demand for scoring systems that are not just accurate but explainable. Regulators require models whose predictions can be understood and audited; banks need interpretability to strengthen customer relationships and satisfy consumer-protection laws; and customers, particularly those with marginal outcomes, want clear reasons for rejection. Explainable artificial intelligence (xAI) also plays a growing role in banks' internal model-monitoring processes, supporting more transparent credit assessment practices. Yet the sheer complexity and variety of models in use today—often described as a "model jungle"—makes it difficult for users and auditors to fully grasp their limitations, risks, and adequacy for sound risk management, underscoring the need for genuine, actionable interpretability.
This paper reviews existing xAI approaches to credit scoring, organizing current model families and post-hoc explanation techniques into a structured taxonomy. Building on this analysis, we propose a set of operational and cognitive evaluation metrics alongside compliance-oriented explainability tests. We conclude by outlining how these elements can be integrated into model deployment, xAI governance frameworks, and the broader software life-cycle management practices of banks.
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