Revolutionizing Regulatory Reporting through AI/ML: Approaches for Enhanced Compliance and Efficiency

Abstract

In the intricate regulatory landscape of today, financial institutions encounter formidable hurdles in meeting reporting mandates while upholding operational efficacy. This study delves into the transformative capacity of Artificial Intelligence (AI) and Machine Learning (ML) technologies in refining regulatory reporting procedures. Through harnessing AI/ML, entities can streamline data aggregation, analysis, and submission, thus fostering enhanced compliance and operational efficiency. Key strategies for integrating AI/ML into regulatory reporting frameworks are discussed, encompassing data standardization, predictive analytics, anomaly detection, and automation. Furthermore, the paper explores the advantages, obstacles, and optimal approaches associated with deploying AI/ML solutions in regulatory reporting. Drawing on real-world illustrations and case studies, this study offers insights into how AI/ML technologies can redefine regulatory reporting practices, empowering financial institutions to adeptly navigate regulatory intricacies while optimizing resource allocation and decision-making processes.