Harnessing the Power of AI for Enhanced Regulatory Compliance and Risk Management in Fintech
DOI:
https://doi.org/10.47941/ijce.1670Keywords:
Artificial Intelligence, Fintech, Regulatory Compliance, Risk Management, Fraud DetectionAbstract
Purpose: This article analyzes how artificial intelligence (AI) is revolutionizing risk management and regulatory compliance in the fintech industry. The objective is to conduct an analysis of AI applications, highlighting how it may be used for proactive risk management, fraud prevention, real-time regulatory monitoring, and risk assessment.
Methodology: Using a literature review methodology, the paper puts together data gathered from multiple sources to give a comprehensive knowledge of how AI is being applied to change the regulatory and risk landscape for fintech. As part of the method, significant works in the subject are reviewed and analyzed, and numerous perspectives are integrated to provide a thorough overview.
Findings: The results highlight how AI significantly improves decision-making processes in response to complicated risk situations and dynamic regulatory contexts while also increasing efficiency and lowering costs. Fintech practices are evolving due to specific applications such as proactive risk management, precise risk assessment, fraud detection, real-time monitoring, and accurate risk management.
Unique contribution to theory, practice and policy: The work adds additional value by combining various AI applications for risk management and regulatory compliance in finance. It provides useful insights for researchers, practitioners, and policymakers by bridging the gap between theory and practice. The article offers industry professionals useful implications in addition to educating the academic community on the complex effects of AI on fintech. It also draws attention to the necessity of flexible regulatory frameworks that can keep up with the fintech industry's rapid advancements in technology, which adds to the policy considerations in this dynamic environment. For individuals negotiating the convergence of artificial intelligence, regulatory compliance, and risk management in the fintech industry, the article is essentially a short and important resource.
Downloads
References
Addo, P. M., Guégan, D., & Hassani, B. K. (2018, April 16). Credit Risk Analysis Using Machine and Deep Learning Models. https://doi.org/10.3390/risks6020038
Agarwal, P. (2019, March 1). Redefining Banking and Financial Industry through the application of Computational Intelligence. https://doi.org/10.1109/icaset.2019.8714305
Al-Shabandar, R., Lightbody, G., Browne, F., Li, J., Wang, H., & Zheng, H. (2019, October 17). The Application of Artificial Intelligence in Financial Compliance Management. https://doi.org/10.1145/3358331.3358339
Aziz, L. A., & Andriansyah, Y. (n.d). The Role of Artificial Intelligence in Modern Banking: An Exploration of AI-Driven Approaches for Enhanced Fraud Prevention, Risk Management, and Regulatory Compliance
Aziz, S., & Dowling, M. (2018, December 7). Machine Learning and AI for Risk Management. https://doi.org/10.1007/978-3-030-02330-0_3
Colladon, A. F., & Remondi, E. (2017, January 1). Using social network analysis to prevent money laundering. https://doi.org/10.1016/j.eswa.2016.09.029
Deshpande, A. (2020, November 26). AI/ML applications and the potential transformation of Fintech and Finserv sectors. https://doi.org/10.1109/cmi51275.2020.9322734
Han, J., Huang, Y., Liu, S., & Towey, K. (2020, June 25). Artificial intelligence for anti-money laundering: a review and extension. https://doi.org/10.1007/s42521-020-00023-1
Hu, X., & Ke, W. (2020, October 1). Bank Financial Innovation and Computer Information Security Management Based on Artificial Intelligence. https://doi.org/10.1109/mlbdbi51377.2020.00120
Leo, M., Sharma, S., & Maddulety, K. (2019, March 5). Machine Learning in Banking Risk Management: A Literature Review. https://doi.org/10.3390/risks7010029
Li, Y., Yi, J., Chen, H., & Peng, D. (2021, January 1). Theory and application of artificial intelligence in financial industry. https://doi.org/10.3934/dsfe.2021006
Liu, D., Zhao, M., & Xu, H. (2021, February 4). Financial Technology Intelligent Intrusion Detection System based on Financial Data Feature Extraction and DNNs. https://doi.org/10.1109/icicv50876.2021.9388459
Maple, C., Szpruch, Ł., Epiphaniou, G., Staykova, K. S., Singh, S. B., Penwarden, W., Wen, Y., Wang, Z., Hariharan, J., & Avramović, P. (2023, August 31). The AI Revolution: Opportunities and Challenges for the Finance Sector. https://arxiv.org/abs/2308.16538
Mill, E., Garn, W., Ryman-Tubb, N., & Turner, C. (2023, January 1). Opportunities in Real-Time Fraud Detection: An Explainable Artificial Intelligence (XAI) Research Agenda. https://doi.org/10.14569/ijacsa.2023.01405121
Poretschkin, M., Schmitz, A., Akila, M., Adilova, L., Becker, D. S., Cremers, A. B., Hecker, D., Houben, S., Mock, M., Rosenzweig, J., Sicking, J., Schulz, E., Voss, A., & Wrobel, S. (2023, June 20). Guideline for Trustworthy Artificial Intelligence -- AI Assessment Catalog. https://arxiv.org/abs/2307.03681
Xie, M. (2019, April 1). Development of Artificial Intelligence and Effects on Financial System. https://doi.org/10.1088/1742-6596/1187/3/032084
Yeo, W. J., Heever, W. V. D., Mao, R., Cambria, E., Satapathy, R., & Mengaldo, G. (2023, September 21). A Comprehensive Review on Financial Explainable AI. https://arxiv.org/abs/2309.11960
Zhou, X., Cheng, S., Zhu, M., Guo, C., Zhou, S., Xu, P., Xue, Z., & Zhang, W. (2018, January 1). A state-of-the-art survey of data mining-based fraud detection and credit scoring. https://doi.org/10.1051/matecconf/201818903002
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Rajath Karangara, Abhishek Shende, Satish Kathiriya
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.