Artificial Intelligence and Machine Learning in Financial Services: A Systematic Literature Review
DOI:
https://doi.org/10.67224/ioasdjbms.2025.v02i03.005Keywords:
artificial intelligence, machine learning, financial services, algorithmic bias, explainable AI, fintech, automated decision-making, financial regulationAbstract
Artificial Intelligence (AI) and Machine Learning (ML) have changed how financial services operate, improving areas like trading, credit assessments, fraud detection, and customer service. These technologies enhance efficiency and customer experience but raise concerns about fairness and regulations. This review looks at research on AI and ML in financial services, focusing on their use, performance, and regulatory challenges from 2018 to 2024. A search of academic databases found 86 relevant studies that analyzed technical implementation, performance, ethics, and market impacts. Results showed that ML in fraud detection can exceed 90% accuracy, and AI in credit scoring can lower prediction errors by 15-25%. Despite these advancements, challenges remain, especially in explainability, bias, and regulation. Successful adoption of AI and ML requires addressing these issues through responsible frameworks and governance structures.
References
• Ajkuna MUJO. (2025). Explainable AI in Credit Scoring: Improving Transparency in Loan Decisions. Journal of Information Systems Engineering and Management, 10(27s), 506–515. https://doi.org/10.52783/jisem.v10i27s.4437
• Apruzzese, G., Colajanni, M., Ferretti, L., Guido, A., & Marchetti, M. (2018). On the effectiveness of machine and deep learning for cyber security. Cyber Security, 1-17. https://doi.org/10.1186/s42400-018-0006-7
• Barocas, S., & Selbst, A. D. (2020). Big data's disparate impact. California Law Review, 104, 671-732. https://doi.org/10.2139/ssrn.2477899
• Bartlett, R., Morse, A., Stanton, R., & Wallace, N. (2022). Consumer-lending discrimination in the FinTech era. Journal of Financial Economics, 143(1), 30-56. https://doi.org/10.1016/j.jfineco.2021.05.047
• Björkegren, D., & Grissen, D. (2020). Behavior revealed in mobile phone usage predicts credit repayment. World Bank Economic Review, 34(3), 618-634. https://doi.org/10.1093/wber/lhx030
• Boissel, C., Derrien, F., Ors, E., & Thesmar, D. (2017). Systemic risk in clearing houses: Evidence from the European repo market. Journal of Financial Economics, 125(3), 511–536. https://doi.org/10.1016/j.jfineco.2017.06.010)
• Canton, H. (2021). Bank for International Settlements—BIS. The Europa Directory of International Organizations 2021, 468–470. https://doi.org/10.4324/9781003179900-72
• CHINCO, A., CLARK‐JOSEPH, A. D., & YE, M. (2018). Sparse Signals in the Cross‐Section of Returns. The Journal of Finance, 74(1), 449–492. Portico. https://doi.org/10.1111/jofi.12733
• D'Acunto, F., Prabhala, N., & Rossi, A. G. (2019). The promises and pitfalls of robo-advising. Review of Financial Studies, 32(5), 1983-2020. https://doi.org/10.1093/rfs/hhz014
• Danielsson, J., Macrae, R., & Uthemann, A. (2019). Artificial Intelligence and Systemic Risk. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3410948
• Dastin, J. (2018). Amazon scraps secret AI recruiting tool that showed bias against Dastin, J. (2022). Amazon Scraps Secret AI Recruiting Tool that Showed Bias against Women Ethics of Data and Analytics, 296–299. https://doi.org/10.1201/9781003278290-44
• Financial Conduct Authority. (2019). Guidance on the use of the regulatory sandbox. London: FCA Innovation Hub.
• Financial Crimes Enforcement Network. (2021). AI and machine learning in BSA compliance. Washington, DC: FinCEN Guidance.
• Financial Stability Board. (2017). Artificial intelligence and machine learning in financial services: Market developments and financial stability implications. Basel: FSB Secretariat.
• Følstad, A., Nordheim, C. B., & Bjørkli, C. A. (2018). What makes users trust a chatbot for customer service? An exploratory interview study. International Conference on Internet Science, 194-208.
• Fredrikson, M., Jha, S., & Ristenpart, T. (2018). Model inversion attacks that exploit confidence information and basic countermeasures. ACM Conference on Computer and Communications Security, 1322-1333.
• Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. Review of Financial Studies, 33(5), 2223-2273. https://doi.org/10.1093/rfs/hhaa009
• Harvey, C. R., & Liu, Y. (2020). Detecting repeatable performance. Review of Financial Studies, 33(5), 2147-2192. https://doi.org/10.1093/rfs/hhz042
• Hendershott, T., & Riordan, R. (2013). Algorithmic trading and the market for liquidity. Journal of Financial and Quantitative Analysis, 48(4), 1001-1024. https://doi.org/10.1017/S0022109013000471
• Huang, J., Chai, J., & Cho, S. (2021). Deep learning in finance and banking: A literature review and classification. Frontiers of Business Research in China, 14(1), 1-24. https://doi.org/10.1186/s11782-020-00082-6
• Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399. https://doi.org/10.1038/s42256-019-0088-2
• Jung, D., Dorner, V., Weinhardt, C., & Pusmaz, H. (2018). Designing a robo-advisor for risk-averse, low-budget consumers. Electronic Markets, 28(3), 367-380. https://doi.org/10.1007/s12525-017-0279-9
• Ke, Z. T., Kelly, B., & Xiu, D. (2019). Predicting Returns With Text Data. National Bureau of Economic Research. https://doi.org/10.3386/w26186
• Kirilenko, A., Kyle, A. S., Samadi, M., & Tuzun, T. (2017). The flash crash: High‐frequency trading in an electronic market. Journal of Finance, 72(3), 967-998. https://doi.org/10.1111/jofi.12498
• Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., ... & Stoyanov, V. (2019). RoBERTa: A robustly optimized BERT pretraining approach. ArXiv Preprint, arXiv:1907.11692.
• Malandri, L., Xing, F. Z., Zhang, Y., Wang, Y., & Orsenigo, C. (2018). Public mood–driven asset allocation: The importance of financial sentiment in portfolio management. Cognitive Computation, 10(6), 1167-1176. https://doi.org/10.1007/s12559-018-9609-2
• Martin, K. (2019). Ethical implications and accountability of algorithms. Journal of Business Ethics, 160(4), 835-850. https://doi.org/10.1007/s10551-018-3921-3
• McKinsey Global Institute. (2024). The age of AI: How artificial intelligence is transforming business. New York: McKinsey & Company.
• McLean, G., & Osei-Frimpong, K. (2019). Hey Alexa... examine the variables influencing the use of artificial intelligent in-home voice assistants. Computers in Human Behavior, 99, 28-37. https://doi.org/10.1016/j.chb.2019.05.009
• Moriano, P., Pendleton, J., Rich, S., & Camp, L. J. (2017). Insider Threat Event Detection in User-System Interactions. Proceedings of the 2017 International Workshop on Managing Insider Security Threats, 1–12. https://doi.org/10.1145/3139923.3139928
• Nzomiwu, A. C. (2025). Decentralized Credit Systems: Transforming Financial Inclusion Through On-Chain Behavioral Data. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5336663
• Nzomiwu, A. C. https://github.com/ChidiTonio/Sentiment_Analysis
• Our Annual Report and Accounts 2019/20. (2019, June 25). FCA. Retrieved September 26, 2205. https://www.fca.org.uk/publications/annual-reports/our-annual-report-and-accounts-2019-20
• Paul, S. (2021). Big Data Analytics. Data Analytics in Marketing, Entrepreneurship, and Innovation, 123–132. https://doi.org/10.1201/9780429196614-7
• Perring, A. (2017). Review of Savage et al. https://doi.org/10.5194/amt-2017-170-rc2
• Gottschalk, P. (2021). Social security by PwC. Private Policing of Economic Crime, 78–84. https://doi.org/10.4324/9781003142607-11
• Selbst, A., Anthony, D., Bambauer, J., Bloch-Wehba, H., Boyd, W., Carlson, A., Cohen, J., Gilman, M., Brennan-Marquez, K., Geczy, I., Glater, J., Guggenberger, N., Hirsch, D., Kaminski, M., Kim, P., Solow-Niederman, A., Loo, R., Verstein, A., Volokh, E., & Waldman, A. (2021). AN INSTITUTIONAL VIEW OF ALGORITHMIC IMPACT ASSESSMENTS. Harvard Journal of Law & Technology, 35. https://jolt.law.harvard.edu/assets/articlePDFs/v35/Selbst-An-Institutional-View-of-Algorithmic-Impact-Assessments.pdf
• Sezer, V., Öncü, S., & Boyraz Baykas, P. (Eds.). (2020). Service Robotics. https://doi.org/10.5772/intechopen.87712
• Verhoef, C., & de Meijer, V. E. (2021). Lever en galwegen. Leerboek Chirurgie, 193–209. https://doi.org/10.1007/978-90-368-2518-4_13
• Yeom, S., Giacomelli, I., Fredrikson, M., & Jha, S. (2018). Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting. 2018 IEEE 31st Computer Security Foundations Symposium (CSF), 268–282. https://doi.org/10.1109/csf.2018.00027
• Zhang, H. (2020). Comment on Zhang et al. 2020. https://doi.org/10.5194/essd-2020-182-rc1
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Copyright (c) 2025 Anthony Chidi Nzomiwu, Francisca Uzooyibo Okoye (Author)
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