Business and Social Sciences
Business and social sciences | Online ISSN 3067-8919
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Volume 3 Number 1 2025
RESEARCH ARTICLE (Open Access)
AI Governance Challenges in Banking: Model Explainability, Regulatory Compliance, and the Limits of Traditional Risk Management
Afsara Tasnim Shama 1*
Business and Social Sciences 3 (1) 1-8 https://doi.org/10.25163/business.3110901
Submitted: 30 December 2024 Revised: 05 March 2025 Accepted: 07 March 2025 Published: 11 March 2025
Abstract
Background: Artificial intelligence has reshaped how banks detect fraud, price credit, and make decisions at a pace few risk frameworks were ever built to handle, and the governance gap this leaves behind is not yet well understood empirically (Truby et al., 2020; Lee et al., 2021).
Methods: A quantitative, cross-sectional survey was administered to 155 professionals drawn from commercial banks, fintech firms, regulatory bodies, AI technology providers, and academia, following an initial distribution of 170 questionnaires (91.2% valid response rate). Six governance constructs — Model Explainability, Regulatory Compliance, Data Governance, Algorithmic Bias, Model Risk, and Cybersecurity Risk — were measured on five-point Likert scales and analyzed in IBM SPSS Statistics 29 using descriptive statistics, Pearson correlation, and multiple linear regression, with multicollinearity assessed via tolerance and VIF.
Results: Model Explainability emerged as the most influential predictor of perceived governance challenges (β = 0.318, r = 0.756, p < .001), followed by Regulatory Compliance (β = 0.264, r = 0.704, p < .001) and Data Governance (β = 0.213, r = 0.671, p = .001). Algorithmic Bias, Model Risk, and Cybersecurity Risk contributed smaller but statistically significant effects. The six-predictor model explained 71.4% of the variance in governance challenges (R² = .714, adjusted R² = .702, F = 61.84), with no evidence of multicollinearity (VIF 1.45–1.92) or autocorrelation (Durbin–Watson = 1.95).
Conclusion: Traditional, financially-oriented risk management frameworks appear insufficiently equipped to govern algorithmic decision-making in banking; explainability and regulatory alignment, rather than technical risk controls alone, seem to anchor perceived governance adequacy. Integrated AI-specific governance frameworks are warranted.
Keywords: Artificial Intelligence Governance; Banking Risk Management; Model Explainability; Regulatory Compliance; Algorithmic Accountability
References
Akyüz, A., & Mavnacioglu, K. (2021). Marketing and financial services in the age of artificial intelligence. In Contributions to finance and accounting (pp. 327–340). https://doi.org/10.1007/978-3-030-68612-3_23
Ala-Pietilä, P., & Smuha, N. A. (2021). A framework for global cooperation on artificial intelligence and its governance. Lecture Notes in Computer Science, 237–265. https://doi.org/10.1007/978-3-030-69128-8_15
Beccalli, E., Elliot, V., & Virili, F. (2020). Artificial intelligence and ethics in portfolio management. In Lecture notes in information systems and organisation (pp. 19–30). https://doi.org/10.1007/978-3-030-47355-6_2
D'Orazio, P. (2021). Towards a post-pandemic policy framework to manage climate-related financial risks and resilience. Climate Policy, 21(10), 1368–1382. https://doi.org/10.1080/14693062.2021.1975623
Ebinger, F., & Omondi, B. (2020). Leveraging digital approaches for transparency in sustainable supply chains: A conceptual study. Sustainability, 12(15), 6129. https://doi.org/10.3390/su12156129
Fenwick, M., Vermeulen, E. P. M., & Corrales, M. (2018). Business and regulatory responses to artificial intelligence: Dynamic regulation, innovation ecosystems and the strategic management of disruptive technology. In Perspectives in law, business and innovation (pp. 81–103). https://doi.org/10.1007/978-981-13-2874-9_4
Gill, K. S. (2020). AI & Society: Editorial volume 35.2: The trappings of AI agency. AI & Society, 35(2), 289–296. https://doi.org/10.1007/s00146-020-00961-9
Hemphill, T. A., & Kelley, K. J. (2021). Artificial intelligence and the fifth phase of political risk management: An application to regulatory expropriation. Thunderbird International Business Review, 63(5), 585–595. https://doi.org/10.1002/tie.22222
Howes, M., Tangney, P., Reis, K., Grant-Smith, D., Heazle, M., Bosomworth, K., & Burton, P. (2014). Towards networked governance: Improving interagency communication and collaboration for disaster risk management and climate change adaptation in Australia. Journal of Environmental Planning and Management, 58(5), 757–776. https://doi.org/10.1080/09640568.2014.891974
Kar, S., Kar, A. K., & Gupta, M. P. (2021). Modeling drivers and barriers of artificial intelligence adoption: Insights from a strategic management perspective. Intelligent Systems in Accounting, Finance and Management, 28(4), 217–238. https://doi.org/10.1002/isaf.1503
Kröger, W. (2020). Automated vehicle driving: Background and deduction of governance needs. Journal of Risk Research, 24(1), 14–27. https://doi.org/10.1080/13669877.2020.1750465
Lee, M. S. A., Floridi, L., & Denev, A. (2021). Innovating with confidence: Embedding AI governance and fairness in a financial services risk management framework. In Philosophical studies series (pp. 353–371). https://doi.org/10.1007/978-3-030-81907-1_20
Magnan, M., & Markarian, G. (2011). Accounting, governance and the crisis: Is risk the missing link? European Accounting Review, 20(2), 215–231. https://doi.org/10.1080/09638180.2011.580943
Mohamed, H., & Yildirim, R. (2021). RegTech and regulatory change management for financial institutions. In Studies in computational intelligence (pp. 153–168). https://doi.org/10.1007/978-3-030-62796-6_8
Nicoletti, B. (2021). Proposition of value and fintech organizations in Banking 5.0. In Palgrave studies in financial services technology (pp. 91–152). https://doi.org/10.1007/978-3-030-75871-4_4
Pagallo, U., Casanovas, P., & Madelin, R. (2019). The middle-out approach: Assessing models of legal governance in data protection, artificial intelligence, and the web of data. The Theory and Practice of Legislation, 7(1), 1–25. https://doi.org/10.1080/20508840.2019.1664543
Rana, T., Wickramasinghe, D., & Bracci, E. (2019). New development: Integrating risk management in management control systems—lessons for public sector managers. Public Money & Management, 39(2), 148–151. https://doi.org/10.1080/09540962.2019.1580921
Sarker, I. H., Furhad, M. H., & Nowrozy, R. (2021). AI-driven cybersecurity: An overview, security intelligence modeling and research directions. SN Computer Science, 2(3). https://doi.org/10.1007/s42979-021-00557-0
Tagde, P., Tagde, P., Tagde, S., Bhattacharya, T., Tagde, P., Tagde, P., Chopra, H., Akter, R., Kaushik, D., & Rahman, M. H. (2021). Blockchain and artificial intelligence technology in e-Health. Environmental Science and Pollution Research, 28(38), 52810–52831. https://doi.org/10.1007/s11356-021-16223-0
Taherdoost, H., & Drazenovic, G. (2024). Impact of artificial intelligence on investment: A narrative review. Algorithms for Intelligent Systems, 275–286. https://doi.org/10.1007/978-981-99-8438-1_20
Truby, J., Brown, R., & Dahdal, A. (2020). Banking on AI: Mandating a proactive approach to AI regulation in the financial sector. Law and Financial Markets Review, 14(2), 110–120. https://doi.org/10.1080/17521440.2020.1760454
Villar, A. S., & Khan, N. (2021). Robotic process automation in banking industry: A case study on Deutsche Bank. Journal of Banking and Financial Technology. https://doi.org/10.1007/s42786-021-00030-9
Wittwehr, C., Blomstedt, P., Gosling, J. P., Peltola, T., Raffael, B., Richarz, A., Sienkiewicz, M., Whaley, P., Worth, A., & Whelan, M. (2019). Artificial intelligence for chemical risk assessment. Computational Toxicology, 13, 100114. https://doi.org/10.1016/j.comtox.2019.100114
Wong, A. (2020). The laws and regulation of AI and autonomous systems. In IFIP advances in information and communication technology (pp. 38–54). https://doi.org/10.1007/978-3-030-64246-4_4
Wong, A. (2021). Ethics and regulation of artificial intelligence. IFIP Advances in Information and Communication Technology, 1–18. https://doi.org/10.1007/978-3-030-80847-1_1
Yigitcanlar, T., Desouza, K., Butler, L., & Roozkhosh, F. (2020). Contributions and risks of artificial intelligence (AI) in building smarter cities: Insights from a systematic review of the literature. Energies, 13(6), 1473. https://doi.org/10.3390/en13061473
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