The Compliance Horizon: Anticipating Regulatory Change in Financial Services and Artificial Intelligence
DOI:
https://doi.org/10.15662/IJRAI.2025.0802011Keywords:
Regulatory compliance, artificial intelligence, financial services, EU AI Act, RegTech, model risk, algorithmic accountability, dynamic compliance architectureAbstract
The convergence of artificial intelligence (AI) and financial services is precipitating a fundamental reconfiguration of the global compliance landscape. This paper examines the emergent regulatory frameworks being developed across major jurisdictions — including the European Union's AI Act, the United States' evolving federal approach, and the United Kingdom's principles-based regime — and analyses their implications for financial institutions navigating an era of algorithmic decision-making, automated risk management, and generative AI deployment.
Drawing on regulatory text analysis, institutional theory, and a comparative policy framework, we argue that traditional compliance architectures are inadequate to address the velocity and opacity of AI-driven financial processes. We identify three structural tensions in contemporary regulation: the innovation-stability paradox, the explainability imperative, and the jurisdictional fragmentation problem. The paper proposes a forward-looking 'Dynamic Compliance Architecture' (DCA) model, offering practitioners and policymakers a structured methodology for anticipating, absorbing, and adapting to regulatory change in real time.
Our findings have significant implications for Chief Compliance Officers, RegTech vendors, central banks, and international standard-setting bodies. We conclude that proactive regulatory horizon-scanning, embedded in institutional governance frameworks, is no longer optional — it is a strategic imperative.
References
1. Adadi, A. & Berrada, M. (2018). Peeking inside the black box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052
2. Anagnostopoulos, I. (2018). FinTech and RegTech: Impact on regulators and banks. Journal of Economics and Business, 100, 7–25.
3. Arner, D.W., Barberis, J. & Buckley, R.P. (2017). FinTech, RegTech, and the reconceptualization of financial regulation. Northwestern Journal of International Law & Business, 37(3), 371–413.
4. Basel Committee on Banking Supervision (BCBS). (2022). Principles for the Sound Management of Operational Risk. Bank for International Settlements.
5. Bommasani, R., Hudson, D.A., Adeli, E. et al. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.
6. Brundage, M., Avin, S., Clark, J. et al. (2018). The malicious use of artificial intelligence: Forecasting, prevention and mitigation. Future of Humanity Institute, University of Oxford.
7. Colaert, V. (2018). RegTech as a response to regulatory expansion in the financial sector. KU Leuven Legal Studies Research Paper, 2018/6.
8. Department for Science, Innovation and Technology (DSIT). (2023). A Pro-Innovation Approach to AI Regulation. UK Government White Paper. HMSO.
9. DiMaggio, P.J. & Powell, W.W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147–160.
10. European Banking Authority (EBA). (2023). Report on Machine Learning for IRB Models. EBA/REP/2023/29. EBA.
11. European Parliament. (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence. Official Journal of the European Union, L 1689/1.
12. Financial Conduct Authority (FCA). (2022). Consumer Duty: Final Rules and Guidance. PS22/9. FCA.
13. Financial Stability Board (FSB). (2022). Supervisory and Regulatory Issues That Merit Authorities' Attention. FSB, Basel.
14. Monetary Authority of Singapore (MAS). (2019). Principles to Promote Fairness, Ethics, Accountability and Transparency (FEAT) in the Use of Artificial Intelligence and Data Analytics in Singapore's Financial Sector. MAS.
15. Office of the Comptroller of the Currency (OCC). (2021). Model Risk Management. Comptroller's Handbook. OCC.
16. Philippon, T. (2019). The FinTech opportunity. In T. Beck & R. Levine (Eds.), Finance, Growth and Inequality. Edward Elgar Publishing.
17. Prudential Regulation Authority (PRA). (2023). Model Risk Management Principles for Banks. SS1/23. Bank of England.
18. Securities and Exchange Commission (SEC). (2023). Conflicts of Interest Associated with the Use of Predictive Data Analytics by Broker-Dealers and Investment Advisers. Proposed Rule, Release No. IA-6353.
19. Wachter, S., Mittelstadt, B. & Russell, C. (2018). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harvard Journal of Law & Technology, 31(2), 841–887.
20. White House. (2023). Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. Executive Order 14110, 30 October 2023.
21. Zetzsche, D.A., Buckley, R.P. & Arner, D.W. (2021). Regulating Libra: The transformative potential of Facebook's cryptocurrency and possible regulatory responses. Oxford Journal of Legal Studies, 41(1), 80–113.
22. Zetzsche, D.A., Buckley, R.P., Arner, D.W. & Barberis, J.N. (2017). Regulating a revolution: From regulatory sandboxes to smart regulation. Fordham Journal of Corporate and Financial Law, 23(1), 31–103.
