설명가능한 인공지능(XAI)의 금융리스크 관리 적용 동향과 과제

초록

The rapid proliferation of artificial intelligence (AI) in the financial sector has elevated Explainable AI (XAI)-technologies that transparently convey model reasoning-to a critical priority in high-risk decision-making domains. This study proposes the E-S-A-R framework (Explainability, Speed, Applicability, Regulatory compliance) to systematically evaluate the practical applicability of leading XAI methods-SHAP, LIME, and PDP-in financial risk management. Through a combined literature review and case studies in credit scoring, investment and trading, and fraud detection, we assessed each method's explainability, computational performance, ease of integration, and regulatory compliance. Our analysis reveals that no single technique can satisfy the complex workflows and evolving regulatory demands of financial practice alone, highlighting the necessity of a complementary, hybrid strategy. We therefore present a hybrid integration approach and detail concrete technical and organizational implementation measures for real-time anomaly detection, transparent decision support, and audit-ready compliance record keeping. This work contributes an integrated evaluation framework for XAI methods and offers actionable guidelines for financial institutions and policymakers to deploy AI ethically and responsibly.

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