Explainable AI for Interpretable Credit Scoring

Regulatory compliance is driving the need for explainable AI in finance: The paper highlights that regulations like GDPR's "right to explanation" and the Equal Credit Opportunity Act (ECOA) now legally require that credit scoring decisions be understandable and transparent, making interpretability not just a technical preference but a legal necessity.

High-performing AI models can be made interpretable without sacrificing accuracy: The research demonstrates that XGBoost models achieved "state-of-the-art performance" on real credit datasets (HELOC and Lending Club) while still being interpretable, challenging the common assumption that accuracy and explainability must be traded off against each other.

Different stakeholders need different types of explanations: The paper proposes a "360-degree explanation framework" offering global, local feature-based, and local instance-based explanations, recognizing that loan officers, regulators, and applicants each need different information to understand why a credit decision was made.

Explainable AI addresses fairness concerns in lending: By making credit scoring models interpretable, the approach helps ensure algorithmic decisions are "understandable and coherent," which is critical for detecting and preventing discriminatory lending practices that could violate fair lending laws.