Explainable AI in Credit Models: Balancing Predictive Power with Transparency

Abstract

The combination of artificial intelligence and machine learning models used as modern credit-scoring systems is more and more based on greater predictive accuracy, but also develops major difficulties in terms of transparency and interpretability. This article will discuss how explainable AI (XAI) methods can be applied to model credit to strike a balance between the performance and transparency needs that regulations like GDPR and the Equal Credit Opportunity Act require. The metamorphosis of the primitive statistical techniques to advanced algorithms has resulted in a conflict between accuracy and interpretability, which is addressed by the XAI methodologies. A range of methods, such as SHAP values, LIME, and counterfactual explanations, allows financial institutions to give meaningful explanations of automated decisions without losing their predictive power. An XAI framework that is extensive in terms of credit evaluation embraces global interpretability (model-wide behavior) and local interpretability (individual decisions), increasing regulatory compliance, widening financial inclusion, and fostering stakeholder trust through the lending ecosystem.