Explainable and Sustainable Artificial Intelligence in Finance: Opportunities, Risks, and Future Innovation
DOI:
https://doi.org/10.63345/sjaibt.v3.i2.201Keywords:
Artificial Intelligence, Financial Services, Machine Learning, Explainable AI, Sustainable FinanceAbstract
Artificial Intelligence is revolutionizing the banking and financial services sector through improved efficiency, data analytics, and innovative customer experience. AI is being used in several applications within the financial services sector such as algorithmic trading, credit scoring, compliance, and fraud detection. The current study emphasizes the opportunities offered by artificial intelligence to the financial sector while simultaneously discussing the associated challenges such as data security, ethics, algorithmic bias, and legal uncertainty. It also pertains to future topics, which include XAI, AI for sustainable finance, RegTech improvements, and hybrid human-AI collaboration. This research proposes to provide a complete evaluation of the prospects and challenges of AI in finance, along with providing recommendations for future studies.
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1. Abi, R. (2025). AI-Driven fraud detection systems in fintech using hybrid supervised and unsupervised learning architectures. International Journal of Research Publication and Reviews, 6(6), 4375–4394. https://doi.org/10.55248/gengpi.6.0625.2161. DOI: https://doi.org/10.55248/gengpi.6.0625.2161
2. Addy, N. W. A., Ajayi-Nifise, N. a. O., Bello, N. B. G., Tula, N. S. T., Odeyemi, N. O., & Falaiye, N. T. (2024). Algorithmic Trading and AI: A Review of Strategies and Market impact. World Journal of Advanced Engineering Technology and Sciences, 11(1), 258–267. https://doi.org/10.30574/wjaets.2024.11.1.0054. DOI: https://doi.org/10.30574/wjaets.2024.11.1.0054
3. Adewuyi, A., Nwangele, C. R., Oladuji, T. J., & Akintobi, A. O. (2023). Advances in Machine Learning for Credit Risk and Underwriting Automation: Emerging Trends in Financial Services. International Journal of Advanced Multidisciplinary Research and Studies, 3(6), 1860-1877. DOI: https://doi.org/10.62225/2583049X.2023.3.6.4405
4. Ahmed, S., Alshater, M. M., Ammari, A. E., & Hammami, H. (2022). Artificial intelligence and machine learning in finance: A bibliometric review. Research in International Business and Finance, 61, 101646. https://doi.org/10.1016/j.ribaf.2022.101646. DOI: https://doi.org/10.1016/j.ribaf.2022.101646
5. Aldboush, H. H. H., & Ferdous, M. (2023). Building Trust in Fintech: An analysis of ethical and privacy considerations in the intersection of big data, AI, and customer trust. International Journal of Financial Studies, 11(3), 90. https://doi.org/10.3390/ijfs11030090. DOI: https://doi.org/10.3390/ijfs11030090
6. Al-Sartawi, A. M. a. M., Hussainey, K., & Razzaque, A. (2022). The role of artificial intelligence in sustainable finance. Journal of Sustainable Finance & Investment, 1–6. https://doi.org/10.1080/20430795.2022.2057405 DOI: https://doi.org/10.1080/20430795.2022.2057405
7. Bahoo, S., Cucculelli, M., Goga, X., & Mondolo, J. (2024). Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis. SN Business & Economics, 4(2). https://doi.org/10.1007/s43546-023-00618-x
8. Bahoo, S., Cucculelli, M., Goga, X., & Mondolo, J. (2024). Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis. SN Business & Economics, 4(2). https://doi.org/10.1007/s43546-023-00618-x. DOI: https://doi.org/10.1007/s43546-023-00618-x
9. Blacklaws, C. (2018). Algorithms: transparency and accountability. Philosophical Transactions of the Royal Society a Mathematical Physical and Engineering Sciences, 376(2128), 20170351. https://doi.org/10.1098/rsta.2017.0351 DOI: https://doi.org/10.1098/rsta.2017.0351
10. Bloomberg. Bloomberg. https://www.bloomberg.com/asia
11. Buchanan, B. G. (2019). Artificial intelligence in finance. In Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.2612537
12. Černevičienė, J., & Kabašinskas, A. (2024). Explainable artificial intelligence (XAI) in finance: a systematic literature review. Artificial Intelligence Review, 57(8). https://doi.org/10.1007/s10462-024-10854-8. DOI: https://doi.org/10.1007/s10462-024-10854-8
13. Chang, V., Xu, Q. A., Akinloye, S. H., Benson, V., & Hall, K. (2024). Prediction of bank credit worthiness through credit risk analysis: an explainable machine learning study. Annals of Operations Research. https://doi.org/10.1007/s10479-024-06134-x DOI: https://doi.org/10.1007/s10479-024-06134-x
14. Elhady, A. M., & Shohieb, S. (2025). AI-driven sustainable finance: computational tools, ESG metrics, and global implementation. Future Business Journal, 11(1). https://doi.org/10.1186/s43093-025-00610-x DOI: https://doi.org/10.1186/s43093-025-00610-x
15. Giudici, P. (2018). Fintech Risk Management: A research challenge for artificial intelligence in finance. Frontiers in Artificial Intelligence, 1. https://doi.org/10.3389/frai.2018.00001 DOI: https://doi.org/10.3389/frai.2018.00001
16. Giudici, P., & Wu, L. (2025). Sustainable artificial intelligence in finance: impact of ESG factors. Frontiers in Artificial Intelligence, 8. https://doi.org/10.3389/frai.2025.1566197. DOI: https://doi.org/10.3389/frai.2025.1566197
17. Goldman Sachs. https://www.goldmansachs.com/
18. Goodell, J. W., Kumar, S., Lim, W. M., & Pattnaik, D. (2021). Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. Journal of Behavioral and Experimental Finance, 32, 100577. https://doi.org/10.1016/j.jbef.2021.100577.
19. Goodell, J. W., Kumar, S., Lim, W. M., & Pattnaik, D. (2021). Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. Journal of Behavioral and Experimental Finance, 32, 100577. https://doi.org/10.1016/j.jbef.2021.100577. DOI: https://doi.org/10.1016/j.jbef.2021.100577
20. 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 DOI: https://doi.org/10.1093/rfs/hhaa009
21. Hafez, I. Y., Hafez, A. Y., Saleh, A., El-Mageed, A. a. A., & Abohany, A. A. (2025). A systematic review of AI-enhanced techniques in credit card fraud detection. Journal of Big Data, 12(1). https://doi.org/10.1186/s40537-024-01048-8. DOI: https://doi.org/10.1186/s40537-024-01048-8
22. Heaton, J. B., Polson, N. G., & Witte, J. H. (2016). Deep learning for finance: deep portfolios. Applied Stochastic Models in Business and Industry, 33(1), 3–12. https://doi.org/10.1002/asmb.2209 DOI: https://doi.org/10.1002/asmb.2209
23. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415. DOI: https://doi.org/10.1126/science.aaa8415
24. Joshi, S. (2025). Gen AI in Financial Cybersecurity: A Comprehensive review of architectures, algorithms, and regulatory challenges. International Journal of Innovations in Science Engineering and Management., 73–88. https://doi.org/10.69968/ijisem.2025v4i373-88. DOI: https://doi.org/10.69968/ijisem.2025v4i373-88
25. JPMorganChase. Retrieved May 15, 2026, from https://www.jpmorganchase.com/
26. Jukl, D., & Lansky, J. (2025). Systematic Review on Algorithmic Trading. Acta Informatica Pragensia. https://doi.org/10.18267/j.aip.276. DOI: https://doi.org/10.18267/j.aip.276
27. Kothandapani, N. H. P. (2024). Automating financial compliance with AI: A New Era in regulatory technology (RegTech). International Journal of Science and Research Archive, 11(1), 2646–2659. https://doi.org/10.30574/ijsra.2024.11.1.0040. DOI: https://doi.org/10.30574/ijsra.2024.11.1.0040
28. Kraus, M., & Feuerriegel, S. (2017). Decision support from financial disclosures with deep neural networks and transfer learning. Decision Support Systems, 104, 38–48. https://doi.org/10.1016/j.dss.2017.10.001 DOI: https://doi.org/10.1016/j.dss.2017.10.001
29. Lawrence, T. S., Oyirinnaya, P., Adesola, A. A., & Iguodala, O. D. (2025). THE CRUCIAL ROLE OF ARTIFICIAL INTELLIGENCE IN FINTECH FOR SUPTECH AND REGTECH SUPERVISION IN BANKING AND FINANCIAL ORGANIZATIONS. International Journal of Artificial Intelligence Research and Development, 3(1), 38–50. https://doi.org/10.34218/ijaird_03_01_003. DOI: https://doi.org/10.34218/IJAIRD_03_01_003
30. Mastercard - Una empresa tecnológica global en la industria de pagos. (n.d.). Mastercard. https://www.mastercard.com/
31. Mhlanga, D. (2020). Industry 4.0 in Finance: The impact of Artificial intelligence (AI) on digital financial inclusion. International Journal of Financial Studies, 8(3), 45. https://doi.org/10.3390/ijfs8030045 DOI: https://doi.org/10.3390/ijfs8030045
32. Misheva, B. H., Osterrieder, J., Hirsa, A., Kulkarni, O., & Lin, S. F. (2021). Explainable AI in credit risk management. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2103.00949.
33. Olowu, N. O., Adeleye, N. a. O., Omokanye, N. a. O., Ajayi, N. a. M., Adepoju, N. a. O., Omole, N. O. M., & Chianumba, N. E. C. (2024). AI-driven fraud detection in banking: A systematic review of data science approaches to enhancing cybersecurity. GSC Advanced Research and Reviews, 21(2), 227–237. https://doi.org/10.30574/gscarr.2024.21.2.0418 DOI: https://doi.org/10.30574/gscarr.2024.21.2.0418
34. Pay, send and save money with PayPal. PayPal. https://www.paypal.com/us/home
35. Quinn, B. (2023). Explaining AI in Finance: past, present, prospects. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2306.02773.
36. Raghavan, N. S. R. (2025). Trustworthy Chatbots in Finance: A framework for explain ability, accessibility, and AI governance. International Journal of Scientific Research in Computer Science Engineering and Information Technology, 11(3), 314–323. https://doi.org/10.32628/cseit2511420. DOI: https://doi.org/10.32628/CSEIT2511420
37. Raj, R., & Kos, A. (2023). Artificial intelligence: evolution, developments, applications, and future scope. PRZEGLĄD ELEKTROTECHNICZNY, 1(2), 3–15. https://doi.org/10.15199/48.2023.02.01 DOI: https://doi.org/10.15199/48.2023.02.01
38. Rashid, A. B., & Kausik, M. a. K. (2024). AI Revolutionizing Industries Worldwide: A comprehensive overview of its diverse applications. Hybrid Advances, 7, 100277. https://doi.org/10.1016/j.hybadv.2024.100277 DOI: https://doi.org/10.1016/j.hybadv.2024.100277
39. Ridzuan, N. N., Masri, M., Anshari, M., Fitriyani, N. L., & Syafrudin, M. (2024). AI in the Financial Sector: The Line between Innovation, Regulation and Ethical Responsibility. Information, 15(8), 432. https://doi.org/10.3390/info15080432. DOI: https://doi.org/10.3390/info15080432
40. Sengupta, P., & Sharma, P. (2023). A study on the role of ai in marketing financial services: Adoption, tools, and impact on customer engagement. International Journal of Computing and Artificial Intelligence, 4(1), 65–70. https://doi.org/10.33545/27076571.2023.v4.i1a.164. DOI: https://doi.org/10.33545/27076571.2023.v4.i1a.164
41. Slack, D., Hilgard, S., Jia, E., Singh, S., & Lakkaraju, H. (2020, February). Fooling lime and shap: Adversarial attacks on post hoc explanation methods. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (pp. 180-186). https://doi.org/10.1145/3375627.3375830. DOI: https://doi.org/10.1145/3375627.3375830
42. Svetlova, E. (2022). AI ethics and systemic risks in finance. AI And Ethics, 2(4), 713–725. https://doi.org/10.1007/s43681-021-00129-1. DOI: https://doi.org/10.1007/s43681-021-00129-1
43. Yadava, N. A. (2023). Ethical and regulatory challenges of AI adoption in the financial services sector: A global perspective. International Journal of Science and Research Archive, 10(1), 1222–1235. https://doi.org/10.30574/ijsra.2023.10.1.0826. DOI: https://doi.org/10.30574/ijsra.2023.10.1.0826
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