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

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  • Harish Padmanaban

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ISSN: 3006-4023 (Online), Vol. 2, Issue 1Journal of Artificial Intelligence General Science (JAIGS)journal homepage: https://ojs.boulibrary.com/index.php/JAIGSRevolutionizing Regulatory Reporting through AI/ML: Approaches forEnhanced Compliance and EfficiencyHarish Padmanaban Ph.D.Site Reliability Engineering lead and Independent Researcher.AbstractIn the intricate regulatory landscape of today, financial institutions encounter formidable hurdles in meeting reportingmandates while upholding operational efficacy. This study delves into the transformative capacity of ArtificialIntelligence (AI) and Machine Learning (ML) technologies in refining regulatory reporting procedures. Throughharnessing AI/ML, entities can streamline data aggregation, analysis, and submission, thus fostering enhancedcompliance and operational efficiency. Key strategies for integrating AI/ML into regulatory reporting frameworksare discussed, encompassing data standardization, predictive analytics, anomaly detection, and automation.Furthermore, the paper explores the advantages, obstacles, and optimal approaches associated with deploying AI/MLsolutions in regulatory reporting. Drawing on real-world illustrations and case studies, this study offers insights intohow AI/ML technologies can redefine regulatory reporting practices, empowering financial institutions to adeptlynavigate regulatory intricacies while optimizing resource allocation and decision-making processes.

Suggested Citation

  • Harish Padmanaban, 2024. "Revolutionizing Regulatory Reporting through AI/ML: Approaches for Enhanced Compliance and Efficiency," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 2(1), pages 71-90.
    • Handle: RePEc:das:njaigs:v:2:y:2024:i:1:p:71-90:id:98
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