Transforming US banking: Leveraging AI-powered technology compliance platform (TCP) for regulatory excellence

Bokka, Veera Venkata Ramana Murthy (2025) Transforming US banking: Leveraging AI-powered technology compliance platform (TCP) for regulatory excellence. World Journal of Advanced Research and Reviews, 25 (1). pp. 2174-2187. ISSN 2581-9615

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Abstract

As artificial intelligence (AI) helps to adopt in technology compliance platforms (TCP), the banking industry can be transformed to be more efficient, scalable and cost effective. In this study, we integrate AI in compliance to delve into its great benefits like automating the routine tasks, taking real time inputs from quantum of infinite amount of data and reduce the operational cost. Financial regulations, as they provide new rules and reliance on addressable data continuously increase in complexity, also provide tremendous scalability possibility to AI systems. Yet the research flags some problems facing the company, including the risk for bias in decision making, as well as an imperative for ethical and regulatory oversight to make sure that decisions are transparent and fair. Since these findings imply that AI driven compliance platforms can enormously enhance the efficacy of regulatory frameworks, they also stress the need to deal with ethical issues and to adapt the capacity of AI systems. Future research of strategies to mitigate algorithmic bias, types of cost effective solutions for smaller institutions, and ways to make AI systems more adaptable to the changing regulations as well as guidelines to make AI more ethical when deployed are recommended. Finally, research priorities outlined in this study will be hugely important when it comes to maximizing the use of AI-based tools in the banking sector to achieve regulatory excellence and to empower the sector to respond to growing demands for compliance.

Item Type: Article
Official URL: https://doi.org/10.30574/wjarr.2025.25.1.3561
Uncontrolled Keywords: Artificial Intelligence; Regulatory compliance; Banking; Compliance platforms; Algorithmic bias; Scalability; Cost-effectiveness; Ethical AI; Financial regulations; Future research
Depositing User: Editor WJARR
Date Deposited: 11 Jul 2025 17:00
Related URLs:
URI: https://eprint.scholarsrepository.com/id/eprint/441