Abstract

In the era of big data and advanced machine learning, financial institutions increasingly rely on artificial intelligence (AI) models for risk assessment. However, the opacity of these "black-box" models poses significant challenges in terms of regulatory compliance, ethical decision-making, and stakeholder trust. Explainable AI (XAI) emerges as a critical solution to enhance transparency without compromising model performance. This article explores the integration of XAI techniques in financial risk assessment models, discussing their benefits, methodologies, implementation challenges, and future prospects. By providing interpretable insights, XAI not only aids in identifying biases and errors but also fosters accountability in high-stakes financial environments. Through case studies and theoretical frameworks, we demonstrate how XAI can transform risk management practices in banking, insurance, and investment sectors.