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Leveraging artificial intelligence in Regulatory Technology (RegTech) for financial compliance
- 1 The University of Queensland, St Lucia QLD 4072, Australia
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Abstract
The rapid development of Regulatory Technology (RegTech) has introduced new methods to handle and simplify the compliance and regulatory issues surrounding financial services and products. Combining artificial intelligence (AI) with this trend is an effective way of strengthening financial services, making them safer and more convenient. The current AI technologies incorporated within RegTech applications improve the efficiency of compliance with rules and regulations, and contribute to the development and implementation of smart technology, including automated regulatory reporting, proven and trustworthy KYC processes, and reduced compliance costs. This paper presents the application of intelligent technology, such as machine learning, deep learning, natural language processing, and smart regulatory frameworks, to build modern, intelligent workflows, dynamically control policies, and instantaneously monitor transactions. We employ empirical economic data, evidenced by realistic case studies, to demonstrate the benefits brought on by intelligent technology to financial institutions, including quicker compliance with regulatory regimes, decreased operational risk, and increased transparency and accountability. Through analysing figures offered in various cases, we show the transformed compliance landscape created by RegTech, how it becomes a solution for the diverse challenges affecting financial sectors on a global scale, and how is it going to impact the whole field of financial services in the future..
Keywords
Regulatory Technology, RegTech, Artificial Intelligence, Financial Compliance, Automated Compliance.
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Cite this article
Liang,P. (2024). Leveraging artificial intelligence in Regulatory Technology (RegTech) for financial compliance. Applied and Computational Engineering,93,166-171.
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Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation
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