Explainable AI for Financial Risk Management: Bridging the Gap Between Black-Box Models and Regulatory Compliance

EasyChair Preprint 14302

Abstract

In the ever-evolving landscape of financial risk management, the integration of artificial intelligence (AI) has revolutionized predictive analytics, enabling unprecedented accuracy and efficiency. However, the predominance of black-box models poses significant challenges for regulatory compliance, transparency, and trust. This paper explores the transformative potential of Explainable AI (XAI) in bridging the gap between sophisticated AI-driven risk assessment and stringent regulatory requirements. We delve into the mechanisms by which XAI elucidates the decision-making processes of complex models, providing clear, interpretable insights into risk predictions. By enhancing transparency, XAI not only facilitates compliance with financial regulations but also fosters greater confidence among stakeholders. Through case studies and empirical analysis, we demonstrate how XAI can be effectively implemented in financial institutions, ensuring that AI systems are both powerful and accountable. This research underscores the critical role of explainability in harmonizing advanced AI methodologies with the need for regulatory adherence and ethical standards in financial risk management.

Keyphrases: (General Data Protection Regulation) GDPR, Artificial Intelligence (AI), Explainable AI (XAI)

@booklet{EasyChair:14302,
  author    = {Abill Robert},
  title     = {Explainable AI for Financial Risk Management: Bridging the Gap Between Black-Box Models and Regulatory Compliance},
  howpublished = {EasyChair Preprint 14302},
  year      = {EasyChair, 2024}}