AI-driven automation for CCAR Regulatory Reporting: A Technical Framework for Financial Institutions

Kothari, Sonali (2025) AI-driven automation for CCAR Regulatory Reporting: A Technical Framework for Financial Institutions. World Journal of Advanced Research and Reviews, 26 (2). pp. 2096-2107. ISSN 2581-9615

This article presents a comprehensive technical framework for implementing artificial intelligence (AI) driven automation in Comprehensive Capital Analysis and Review (CCAR) regulatory reporting for financial institutions. The framework addresses the growing challenges of regulatory complexity, data integration, and operational burden faced by banks in maintaining capital adequacy compliance. Through a structured approach encompassing data integration, analytical processing, and regulatory intelligence capabilities, the article demonstrates how AI technologies can transform traditional compliance processes. Machine learning for data validation, natural language processing for regulatory interpretation, and predictive analytics for stress testing collectively enable significant improvements in accuracy, efficiency, and risk management. The implementation methodology outlined offers a phased deployment strategy complemented by governance structures and organizational alignment considerations, delivering measurable performance enhancements, risk mitigation benefits, and strategic advantages for forward-thinking financial institutions. Looking forward, AI-driven CCAR automation will likely evolve toward increasingly adaptive systems that integrate with broader regulatory technologies, enabling financial institutions to respond more fluidly to evolving compliance demands while optimizing capital management strategies.