Explainable AI in Credit Decisioning: Balancing Accuracy and Transparency

The integration of artificial intelligence (AI) into credit decisioning has significantly enhanced the accuracy and efficiency of credit risk assessment, enabling financial institutions to process vast volumes of applicant data and detect complex patterns beyond the capacity of traditional statistical models. However, the growing reliance on high-performance yet opaque “black box” algorithms, such as deep learning and ensemble methods, has raised concerns over interpretability, fairness, and regulatory compliance. Explainable AI (XAI) emerges as a critical paradigm for addressing these challenges, offering methodologies that make model outputs understandable to both technical and non-technical stakeholders without undermining predictive performance. This examines the inherent trade-off between accuracy and transparency in AI-driven credit scoring, analyzing the capabilities and limitations of interpretable models (e.g., logistic regression, decision trees) and model-agnostic explanation techniques (e.g., LIME, SHAP, counterfactual analysis). This situates XAI within the context of legal frameworks such as the EU’s General Data Protection Regulation (GDPR) “Right to Explanation” and the Basel Committee’s risk management principles, emphasizing its role in fostering trust, mitigating bias, and supporting fair lending practices. Case studies from banking and fintech sectors illustrate practical implementations, demonstrating how hybrid approaches can preserve the benefits of advanced machine learning while meeting transparency requirements. Challenges remain, including explanation fidelity, scalability, and alignment between technical justifications and regulatory expectations. The findings suggest that adopting XAI in credit decisioning is not only feasible but also strategically advantageous for improving customer confidence, enhancing compliance, and promoting responsible innovation in financial services. Future research should focus on developing standardized explainability metrics, advancing interpretable deep learning, and embedding XAI into governance frameworks to balance the dual imperatives of accuracy and transparency.