Volume 6 Number 1 2025
Ethical and Regulatory Challenges of Deploying AI Powered Credit Management Systems in Banks
Kripa Nath Dey1, Romana Akhter2, Farzana Akter3, Md Maruf Hossain4, Sk Md Zafar Iqbal5
Journal of Primeasia 6 (1) 1-8 https://doi.org/10.25163/primeasia.6110351
Submitted: 29 June 2025 Revised: 07 September 2025 Published: 08 September 2025
Abstract
Background: The application of Artificial Intelligence (AI) in banking has transformed credit management by enhancing efficiency, accuracy, and risk assessment. However, these advancements also raise critical ethical and regulatory concerns, including transparency, bias, data privacy, and compliance with existing laws. Methods: This quantitative study explored the ethical and regulatory aspects of AI-driven credit management systems. A structured questionnaire was administered to employees of 15 commercial banks in Dhaka and Chittagong. Out of 500 distributed questionnaires, 400 valid responses were analyzed using descriptive statistics, reliability testing, and multiple regression analysis to test the proposed model. Results: Findings revealed that ethical challenges, particularly bias mitigation and decision transparency, were rated as more pressing than regulatory issues. Both ethical and regulatory constructs significantly predicted perceived risks associated with AI adoption. Weaknesses in regulatory frameworks were found to exacerbate operational risks by limiting oversight. AI-adopting banks demonstrated stronger strategic responses, including training, audits, and governance practices, compared to non-adopting banks. Conclusion: The study highlights the need for robust governance mechanisms, routine AI audits, and stronger collaboration with regulators to ensure responsible AI deployment in credit management. Addressing ethical risks and strengthening regulatory frameworks are critical to mitigating operational and reputational risks while fostering sustainable adoption of AI in the banking sector.
Keywords: Artificial Intelligence, Credit Management, Regulatory Compliance, Ethical Challenges, Banking System.
References
Akhter, N. (2023). Determinants of commercial bank’s non-performing loans in Bangladesh: An empirical evidence. Cogent Economics & Finance. https://doi.org/10.1080/23322039.2023.2194128
Alam, N. (2024). Improving IT Control Compliance using Artificial Intelligence. International Journal of Computer Science and Mobile Computing, 13(5), 43–46. https://doi.org/10.47760/ijcsmc.2024.v13i05.003
Alzaabi, M., & Shuhaiber, A. (2022). The role of the AI availability and perceived risks on AI adoption and organizational values. Intelligent Human Systems Integration (IHSI 2022): Integrating People and Intelligent Systems, 22(22). https://doi.org/10.54941/ahfe1001043
Atadoga, A., Obi, O. C., Onwusinkwue, S., Dawodu, S. O., Osasona, F., Daraojimba, A. I., Atadoga, A., Obi, O. C., Onwusinkwue, S., Dawodu, S. O., Osasona, F., & Daraojimba, A. I. (2024). AI’s evolving impact in US banking: An insightful review. International Journal of Science and Research Archive, 11(1), Article 1. https://doi.org/10.30574/ijsra.2024.11.1.0157
Bücker, M., Szepannek, G., Gosiewska, A., & Biecek, P. (2022). Transparency, auditability, and explainability of machine learning models in credit scoring. Journal of the Operational Research Society. https://www.tandfonline.com/doi/abs/10.1080/01605682.2021.1922098
Cardozo, G. T. (2023). Approach to global regulations around AI. LatIA, 1, 7–7. https://doi.org/10.62486/latia20237
Cochran, W. G. (1942). Sampling Theory When the Sampling-Units are of Unequal Sizes. Journal of the American Statistical Association, 199–212.
Daníelsson, J., Macrae, R., & Uthemann, A. (2022). Artificial intelligence and systemic risk. Journal of Banking & Finance, 140, 106290. https://doi.org/10.1016/j.jbankfin.2021.106290
Deshmukh, P. B., Nikam, P., & Moholkar, N. (2024). ROLE OF ARTIFICIAL INTELLIGENCE IN FINANCIAL MANAGEMENT. ShodhKosh: Journal of Visual and Performing Arts, 5(6), 541–557. https://doi.org/10.29121/shodhkosh.v5.i6.2024.1765
Dhawas, P., Katankar, V. K., Gorakh, J., Shende, R. R., Madankar, M., Sahni, T., Dhawas, P., Katankar, V. K., Gorakh, J., Shende, R. R., Madankar, M., & Sahni, T. (2025). AI and Its Transformative Role in Financial Services (ai-and-its-transformative-role-in-financial-services) [Chapter]. Https://Services.Igi-Global.Com/Resolvedoi/Resolve.Aspx?Doi=10.4018/979-8-3693-8507-4.Ch013; IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-8507-4.ch013
Hrabariev, A., Derbentsev, V., Rososhenko, V., Myslynskyi, D., & Fedorenko, O. (2024). AI IN BANKING: CURRENT APPLICATIONS, CHALLENGES, AND PROSPECTS. ?????? ? ?????????? ? ?????, 6(6). https://doi.org/10.52058/3041-1254-2024-6(6)-658-674
Hussain, A., Khan.N, M. A., S, A. A. K., & Kousarziya. (2024). Enhancing Credit Scoring Models with Artificial Intelligence: A Comparative Study of Traditional Methods and AI-Powered Techniques. QTanalytics Publication (Books), 99–107. https://doi.org/10.48001/978-81-966500-8-7-10
Jain, L. R., & Menon, V. (2023). AI Algorithmic Bias: Understanding its Causes, Ethical and Social Implications. 2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI), 460–467. https://doi.org/10.1109/ICTAI59109.2023.00073
Liu, P. (2024). Optimizing E-commerce Operational Efficiency and Customer Experience Through Data Analytics and Technological Innovation. Advances in Economics, Management and Political Sciences, 100, 14–19. https://doi.org/10.54254/2754-1169/100/20241121
Majumder, T. (2023). The Evaluating Impact of Artificial Intelligence on Risk Management and Fraud Detection in the Commercial Bank in Bangladesh. International Journal of Applied and Natural Sciences, 1(1), 67–76. https://doi.org/10.61424/ijans.v1i1.75
Matai, & Matai, P. (2024). The Imperative of AI Governance: Steering BFSI towards Responsible Innovation. Journal of Artificial Intelligence & Cloud Computing, 1–5. https://doi.org/10.47363/JAICC/2024(3)319
Mazzini, G., & Bagni, F. (2023). Considerations on the regulation of AI systems in the financial sector by the AI Act. Frontiers in Artificial Intelligence, 6. https://doi.org/10.3389/frai.2023.1277544
More, M., Shah, S., Shelke, A., Behare, N., More, M., Shah, S., Shelke, A., & Behare, N. (2025). Ethical and Regulatory Considerations in AI Adoption Within Legal Systems (ethical-and-regulatory-considerations-in-ai-adoption-within-legal-systems) [Chapter]. Https://Services.Igi-Global.Com/Resolvedoi/Resolve.Aspx?Doi=10.4018/979-8-3693-7205-0.Ch009; IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-7205-0.ch009
Pillai, R. P., Latha, D. P. P., Pillai, R. P., & Latha, D. P. P. (2025). Study on Application of Artificial Intelligence and Machine Learning in the Banking Sector for Fraud Detection and Prevention (study-on-application-of-artificial-intelligence-and-machine-learning-in-the-banking-sector-for-fraud-detection-and-prevention) [Chapter]. Https://Services.Igi-Global.Com/Resolvedoi/Resolve.Aspx?Doi=10.4018/979-8-3693-3940-4.Ch017; IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-3940-4.ch017
Qureshi, N. I., Choudhuri, S. S., Nagamani, Y., Varma, R. A., & Shah, R. (2024). Ethical Considerations of AI in Financial Services: Privacy, Bias, and Algorithmic Transparency. 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS), 1, 1–6. https://doi.org/10.1109/ICKECS61492.2024.10616483
Sadok, H., Sakka, F., & Maknouzi, M. E. H. E. (2022). Artificial intelligence and bank credit analysis: A review. Cogent Economics & Finance. https://www.tandfonline.com/doi/abs/10.1080/23322039.2021.2023262
Silva, N. S. e. (2024). The Artificial Intelligence Act: Critical overview (arXiv:2409.00264). arXiv. https://doi.org/10.48550/arXiv.2409.00264
Singh, D. R. (2024). Contribution of culture and religion in the philosophical tradition. Knowledgeable Research A Multidisciplinary Journal, 3(01), 37–43.
Syed, Z., Okegbola, O., Akiotu, C. A., Syed, Z., Okegbola, O., & Akiotu, C. A. (2025). Utilising Artificial Intelligence and Machine Learning for Regulatory Compliance in Financial Institutions (utilising-artificial-intelligence-and-machine-learning-for-regulatory-compliance-in-financial-institutions) [Chapter]. Https://Services.Igi-Global.Com/Resolvedoi/Resolve.Aspx?Doi=10.4018/979-8-3693-5966-2.Ch010; IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-5966-2.ch010
Thais, S., Shumway, H., & Saragih, A. I. (2023). Algorithmic bias: Looking beyond data bias to ensure algorithmic accountability and equity. MIT Science Policy Review, 4. https://sciencepolicyreview.pubpub.org/pub/y5f5imu5/release/2
Thoom, S. R. (2025). Lessons from AI in finance: Governance and compliance in practice. International Journal of Science and Research Archive, 14(1), 1387–1395. https://doi.org/10.30574/ijsra.2025.14.1.0235