Evaluating Compliance Readiness with Responsible AI Regulation in Financial Technology Firms

Authors

  • Yan-Tung Ip School of Journalism and Communication, Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China Author
  • Carol Lee School of Journalism and Communication, Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China Author

Keywords:

Responsible Artificial Intelligence, Difference Analysis, Financial Technology, Regulatory Compliance, Algorithmic Transparency

Abstract

The rapid integration of artificial intelligence within the financial technology sector has necessitated the development of rigorous regulatory frameworks aimed at ensuring ethical, transparent, and fair algorithmic decision-making. This paper provides a comprehensive evaluation of compliance readiness among financial technology firms through the application of difference analysis. As regulatory bodies globally transition from voluntary ethical guidelines to binding legal mandates, organizations face significant operational and technical challenges in aligning their existing technological infrastructures with emerging responsible artificial intelligence regulations. By utilizing difference analysis, this study systematically maps current institutional practices against anticipated regulatory requirements, identifying critical discrepancies in areas such as algorithmic transparency, bias mitigation, and automated accountability mechanisms. The findings reveal a persistent gap between high-level policy commitments and granular technical implementation, particularly regarding the interpretability of complex machine learning models utilized in credit scoring and algorithmic trading. Furthermore, this research highlights systemic vulnerabilities in data governance protocols that impede comprehensive compliance. The insights derived from this analysis provide actionable strategies for financial technology firms to proactively re-engineer their compliance architectures. Ultimately, this paper contributes to the broader academic discourse on regulatory technology by establishing a standardized methodological framework for quantifying compliance readiness in rapidly evolving regulatory landscapes.

References

1. Zhang, Y.; Joe, I. The Optimized Deployment of Service Function Chain Based on Deep Reinforcement Learning Algorithm. In Proceedings of the International Conference on Machine Learning, Pattern Recognition and Automation Engineering, Singapore, 7–9 August 2024; ACM: New York, NY, USA, 2024; pp. 24–28.

2. Foerster, J.N.; Farquhar, G.; Afouras, T.; Nardelli, N.; Whiteson, S. Counterfactual Multi-Agent Policy Gradients. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018), New Orleans, LA, USA, 2–7 February 2018; AAAI Press: Palo Alto, CA, USA, 2018; pp. 2974–2982.

3. Nguyen, T.T.; Nguyen, N.D.; Nahavandi, S. Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications. IEEE Trans. Cybern. 2020, 50, 3826–3839.

4. Chen, A.C.H.; Jia, W.-K.; Hwang, F.-J.; Liu, G.; Song, F.; Pu, L. Machine Learning and Deep Learning Methods for Wireless Network Applications. EURASIP J. Wirel. Commun. Netw. 2022, 2022, 115.

5. Latreche, S.; Bellahsene, H. A Comprehensive Survey on 6G: Enabling Technologies, Key Applications, and Future Challenges. Frankl. Open 2026, 15, 100559.

6. Sun, A.; Wu, P.; Jin, S.; Jiao, F. Deep Reinforcement Learning-Driven Multidomain Resource Allocation for Integrated Sensing, Communication, and Computing. In Proceedings of the Eighth International Conference on Artificial Intelligence and Pattern Recognition (AIPR 2025), Quanzhou, China, 19–21 December 2025; Tian, H., Ed.; SPIE: Bellingham, WA, USA, 2025; p. 239.

7. Bagdasaryan, E.; Veit, A.; Hua, Y.; Estrin, D.; Shmatikov, V. How To Backdoor Federated Learning. In Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics; PMLR: Cambridge, MA, USA, 2020; pp. 2938–2948.

8. Li, S.; Fang, B. AI Development Strategies in Countries Around the World. In Artificial Intelligence Security and Safety; Fang, B., Ed.; Springer Nature: Singapore, 2025; pp. 51–84.

9. Lee, T.H.; Kim, J. Hybrid PPO–DQN for Multi-Objective Adaptive Cruise Control in Eco-Driving: Reward Shaping Toward Safety and Sustainability (Student Abstract). In Proceedings of the AAAI Conference on Artificial Intelligence, Singapore, 20–27 January 2026.

10. Pandya, S.; Srivastava, G.; Jhaveri, R.; Babu, M.R.; Bhattacharya, S.; Maddikunta, P.K.R.; Mastorakis, S.; Piran, M.J.; Gadekallu, T.R. Federated Learning for Smart Cities: A Comprehensive Survey. Sustain. Energy Technol. Assess. 2023, 55, 102987.

11. Ozdalga, E.; Ozdalga, A.; Ahuja, N. The smartphone in medicine: A review of current and potential use among physicians and students. J. Med. Internet Res. 2012, 14, e128.

12. Charani, E.; Castro-Sánchez, E.; Moore, L.S.; Holmes, A. Do smartphone applications in healthcare require a governance and legal framework? It depends on the application! BMC Med. 2014, 12, 29.

13. Hoang, D.T.; Huynh, N.V.; Nguyen, D.N.; Hossain, E.; Niyato, D. Deep Reinforcement Learning for Wireless Communications and Networking: Theory, Applications and Implementation, 1st ed.; Wiley: Hoboken, NJ, USA, 2023.

14. Wang, Y.; Lei, J.; Shang, F.; Li, Y. A Comprehensive Survey of Multi-Agent Deep Reinforcement Learning for Wireless Spectrum Management. Neurocomputing 2025, 653, 131236.

15. Bereketeab, L.; Zekeria, A.; Aloqaily, M.; Guizani, M.; Debbah, M. Energy Optimization in Sustainable Smart Environments with Machine Learning and Advanced Communications. IEEE Sens. J. 2024, 24, 5704–5712.

16. Omidshafiei, S.; Pazis, J.; Amato, C.; How, J.P.; Vian, J. Deep Decentralized Multi-Task Multi-Agent Reinforcement Learning under Partial Observability. In Proceedings of the 34th International Conference on Machine Learning (ICML 2017), Sydney, Australia, 6–11 August 2017; PMLR: New York, NY, USA, 2017; Volume 70, pp. 2681–2690.

17. Lim, W.Y.B.; Luong, N.C.; Hoang, D.T.; Jiao, Y.; Liang, Y.-C.; Yang, Q.; Niyato, D.; Miao, C. Federated Learning in Mobile Edge Networks: A Comprehensive Survey. IEEE Commun. Surv. Tutor. 2020, 22, 2031–2063.

18. Shi, W.; Cao, J.; Zhang, Q.; Li, Y.; Xu, L. Edge Computing: Vision and Challenges. IEEE Internet Things J. 2016, 3, 637–646.

19. Al-Fuqaha, A.; Guizani, M.; Mohammadi, M.; Aledhari, M.; Ayyash, M. Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Commun. Surv. Tutor. 2015, 17, 2347–2376.

20. Batty, M.; Axhausen, K.W.; Giannotti, F.; Pozdnoukhov, A.; Bazzani, A.; Wachowicz, M.; Ouzounis, G.; Portugali, Y. Smart Cities of the Future. Eur. Phys. J. Spec. Top. 2012, 214, 481–518.

21. Mahmood, A.; Abdallah, A.M.; Baharom, B.B.; Habilah, A.S.K. Ensuring Satellite Operational Integrity: A Power Budget Analysis for Next Generation Satellites. IEEE Access 2025, 13, 44901–44911.

22. Li, Y.; Chang, Y.; Fukawa, K.; Kodama, N. Reinforcement Learning-Based Cognitive Radio Transmission Scheduling in Vehicular Systems. In Proceedings of the 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring), Florence, Italy, 20–23 June 2023; pp. 1–5.

23. Pessoa, L. (2008). On the relationship between emotion and cognition. Nature Reviews Neuroscience, 9(2), 148–158.

24. Zendejo, D. S., & Escalona, R. S. (2025). Analysis of best practices compliance and transparency for diamond open access journals. Revista de Comunicacion de la Seeci, 58, e903.

25. Ferrag, M.A.; Friha, O.; Kantarci, B.; Tihanyi, N.; Cordeiro, L.; Debbah, M.; Hamouda, D.; Al-Hawawreh, M.; Choo, K.-K.R. Edge Learning for 6G-Enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses. IEEE Commun. Surv. Tutor. 2023, 25, 2654–2713.

26. Scott, W.R. Constructing an analytic framework I: Three pillars of institutions. In Institutions and Organizations, 2nd ed.; SAGE Publications: Thousand Oaks, CA, USA, 2001; pp. 47–70.

27. Rashid, T.; Samvelyan, M.; Schroeder de Witt, C.; Farquhar, G.; Foerster, J.N.; Whiteson, S. QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning. In Proceedings of the 35th International Conference on Machine Learning (ICML 2018), Stockholm, Sweden, 10–15 July 2018; PMLR: New York, NY, USA, 2018; Volume 80, pp. 4295–4304.

28. Coombs, W. T. (2007). Protecting organization reputations during a crisis: The development and application of situational crisis communication theory. Corporate Reputation Review, 10(3), 163–176.

29. Kim, S.Y.; Swann, W.L.; Weible, C.M.; Bolognesi, T.; Krause, R.M.; Park, A.Y.S.; Tang, T.; Maletsky, K.; Feiock, R.C. Updating the Institutional Collective Action Framework. Policy Stud. J. 2022, 50, 9–34.

Downloads

Published

2026-03-30

Issue

Vol. 1 No. 2 (2026)

Section

Articles

License

Copyright (c) 2026 Authors

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Articles are distributed under the Creative Commons Attribution 4.0 International License (CC BY 4.0), unless otherwise stated.