AI and Financial Model Risk Management: Applications, Challenges, Explainability, and Future Directions

The rapid adoption of AI/ML models in high-stakes domains like finance and healthcare has intensified concerns around model risk—ranging from biases and opacity to regulatory non-compliance. This paper explores the transformative impact of AI on risk management, focusing on its applications in predictive analytics, credit risk assessment and regulatory compliance. We also discuss challenges such as model risk, ethical concerns, and regulatory hurdles, and propose future directions for responsible AI adoption. This paper synthesizes insights from 50+ sources to analyze AI Model Risk Management (AIMRM) frameworks, including regulatory guidelines (e.g., NIST AI RMF, EU AI Act), governance strategies, and technical mitigation techniques. We highlight emerging best practices for financial institutions and propose future directions to enhance model robustness, explainability, and compliance. The increasing adoption of artificial intelligence (AI) in financial risk management has highlighted the critical need for explainable AI (XAI) solutions. This paper provides a comprehensive examination of AI applications across credit risk assessment, fraud detection, and regulatory compliance, discusses quantitative improvements including 15-20\% accuracy gains in predictive modeling and 15-30\% reductions in false positives found in various literature. We analyze the critical role of Explainable AI (XAI) techniques like SHAP and LIME in addressing model opacity while maintaining performance stability (AUC-ROC within $\pm$0.03 as suggested by current literature). The study highlights unique risks posed by Generative AI (GenAI), including data provenance issues, adversarial vulnerabilities, and regulatory compliance challenges, proposing adapted Model Risk Management (MRM) frameworks that account for the complete AI lifecycle. Through synthesis of 50+ academic and industry sources, we identify persistent gaps in standardization (50-65\% of firms lack version control for feature stores) and monitoring (only 30-40\% implement real-time data pipeline surveillance). The paper concludes with best practices for hybrid modeling approaches that combine traditional and AI-driven techniques, emphasizing the need for robust governance structures, continuous model monitoring, and regulatory alignment to ensure responsible deployment in financial institutions.