Aileni, Anvesh Reddy (2025) Navigating the regulatory landscape: The emergence of AI-powered compliance agents. World Journal of Advanced Research and Reviews, 26 (2). pp. 3328-3333. ISSN 2581-9615
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Abstract
Regulatory Compliance Agents powered by artificial intelligence are transforming how enterprises navigate increasingly complex regulatory environments. These intelligent systems autonomously monitor, interpret, and implement regulatory requirements across multiple jurisdictions, offering unprecedented efficiency improvements in compliance management. By leveraging advanced natural language processing and machine learning technologies, these agents demonstrate remarkable capabilities in processing regulatory documents, identifying relevant changes, and translating abstract requirements into concrete operational controls. Implementation across diverse industries including financial services, healthcare, energy, and pharmaceuticals shows consistent benefits in reducing compliance costs, improving accuracy, and accelerating regulatory response times. While offering transformative potential, these solutions face challenges related to interpretive accuracy, regulatory acceptance, and technical integration. Organizations implementing these systems must carefully address governance frameworks and human-agent collaboration models to maximize benefits while maintaining appropriate oversight. As regulatory landscapes continue to evolve, AI-powered compliance agents represent not merely technological innovations but strategic assets that fundamentally reshape how organizations approach compliance management, enabling more proactive, efficient, and comprehensive regulatory navigation in an increasingly regulated global economy.
Item Type: | Article |
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Official URL: | https://doi.org/10.30574/wjarr.2025.26.2.1923 |
Uncontrolled Keywords: | Regulatory compliance; Artificial intelligence; Natural language processing; Automated monitoring; Governance frameworks |
Depositing User: | Editor WJARR |
Date Deposited: | 20 Aug 2025 11:34 |
Related URLs: | |
URI: | https://eprint.scholarsrepository.com/id/eprint/3418 |