Pioneering AI-Driven Fraud Detection and AML Strategies: Transforming Azerbaijan's Banking Landscape through Innovative Machine Learning Algorithms and Behavioral Analytics
Keywords:
AI-driven, fraud detection AML , Anti-Money Laundering, machine learning algorithms, behavioral analytics, Azerbaijan's banking landscapeAbstract
The essay aims to examine how AI-based strategies for fraud detection and AML in Azerbaijan’s banking establishments are potentially capable of playing a transformational role. It explores the fact that fraud and anti-money laundering (AML) are current issues and, hence, provides the reader with novel machine learning algorithms and behavior analytics built by the author. Research shows that these methods are good at discerning fraud and identifying people who are sly. The paper also covers the matter of implementation barriers and presents ideas for successful implementation, creating a better way for more secure and effective banks in the Azerbaijani context.
Downloads
References
Alessio Faccia (2023). Self-assessment toolkit for energy anti-money laundering: Unveiling key lessons from high-profile case studies. The Journal of World Energy Law & Business, 16(5), pp.387–413. doi: https://doi.org/10.1093/jwelb/jwad013.
Alevizos, L. and Dekker, M. (2024). Towards an ai-enhanced cyber threat intelligence processing pipeline. arXiv (Cornell University), 1(2024), pp.1–6. doi: https://doi.org/10.48550/arxiv.2403.03265.
Council of Europe (2024). Azerbaijan Should Step up Investigations and Prosecutions of Money Laundering and Improve Supervisory arrangements, Says MONEYVAL - Committee of Experts on the Evaluation of Anti-Money Laundering Measures and the Financing of Terrorism - www.coe.int. [online] Committee of Experts on the Evaluation of Anti-Money Laundering Measures and the Financing of Terrorism. Available at: https://www.coe.int/en/web/moneyval/-/azerbaijan-should-step-up-investigations-and-prosecutions-of-money-laundering-and-improve-supervisory-arrangements-says-moneyval [Accessed 9 Apr. 2024].
David Ríos Insua, Roi Naveiro, Gallego, V. and Poulos, J. (2023). Adversarial machine learning: Bayesian perspectives. Journal of the American Statistical Association, 118(543), pp.1–12. doi: https://doi.org/10.1080/01621459.2023.2183129.
Jugal Kishor, Sharma, S. and Sylva Alif Rusmita (2024). A review and theoretical foundation for future study on how artificial intelligence is affecting the societal-financial interaction. Advances in finance, accounting, and economics book series (Print), 1(2023), pp.32–57. doi: https://doi.org/10.4018/979-8-3693-0082-4.ch003.
Lokanan, M.E. (2022). Predicting money laundering using machine learning and artificial neural networks algorithms in banks. Journal of Applied Security Research, 19(1), pp.1–25. doi: https://doi.org/10.1080/19361610.2022.2114744.
Nandi, A.K., Randhawa, K.K., Chua, H.S., Seera, M. and Lim, C.P. (2022). Credit Card Fraud Detection Using a Hierarchical behavior-knowledge Space Model. PLOS ONE, 17(1), pp.3–4. doi: https://doi.org/10.1371/journal.pone.0260579.
None Paulin Kamuangu (2024). Digital transformation in finance: A review of current research and future directions in fintech. World Journal Of Advanced Research and Reviews, 21(3), pp.1667–1675. doi: https://doi.org/10.30574/wjarr.2024.21.3.0904.
Shahbazov, I., Afandiyev, Z. and Balayeva, A. (2021). Some Determinants of Crime Reporting among Economic and Financial Crime Victims: the Case of Azerbaijan. Journal of White Collar and Corporate Crime, 4(1), p.2631309X2110379. doi: https://doi.org/10.1177/2631309x211037922.
Tanuwijaya, F., Fatimah Zulfa Salsabilla, M. Arief Amrullah and Dina Tsalist Wildana (2023). The urgency of regulating the use of artificial intelligence in detecting suspicious financial transactions. Advances in social science, education and humanities research, 1(2023), pp.1066–1079. doi: https://doi.org/10.2991/978-2-38476-164-7_99.
Tillu, R., Muthusubramanian, M. and Periyasamy, V. (2023). Transforming regulatory reporting with AI/ML: Strategies for compliance and efficiency. Journal of knowledge learning and science technology, 2(1), pp.145–157. doi: https://doi.org/10.60087/jklst.vol2.n1.p157.
Tiwari, M., Ferrill, J. and Mehrotra, V. (2022). Using graph database platforms to fight money laundering: Advocating large scale adoption. Journal of Money Laundering Control, 26(3), pp.474–487. doi: https://doi.org/10.1108/jmlc-03-2022-0047.
Treleaven, P., Smietanka, M. and Pithadia, H. (2022). Federated learning: The pioneering distributed machine learning and privacy-preserving data technology. Computer, 55(4), pp.20–29. doi: https://doi.org/10.1109/mc.2021.3052390.
Truby, J., Brown, R. and Dahdal, A. (2020). Banking on AI: Mandating a proactive approach to AI regulation in the financial sector. Law and Financial Markets Review, [online] 14(2), pp.110–120. doi: https://doi.org/10.1080/17521440.2020.1760454.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Ramin Abbasov
This work is licensed under a Creative Commons Attribution 4.0 International License.
