AI-Driven Data Governance for AML/KYC in Credit Card Issuance: A Framework to Reduce Regulatory Consent Orders

Authors

  • Sanjay Chandrakant Vichare N.L. Dalmia Institute of Management Studies and Research, Mumbai, Maharashtra, India

DOI:

https://doi.org/10.22399/ijcesen.3757

Keywords:

AI governance, AML, KYC, credit card issuance, data governance, fraud detection

Abstract

In the very digitalized financial world today, the convergence of artificial intelligence (AI) and data governance can transform anti-money laundering (AML) and know-your-customer (KYC) compliance in credit card issuing. This paper discusses how AI-governance models can assistin closing regulatory gaps, enhance operational performance, and reduce the likelihood of regulatory consent orders. Based on current research and AML/KYC industry applications, the paper determines the most common AI techniques in AML/KYC to include machine learning, natural language processing, and explainable AI.

References

[1] FATF. (2023). Anti-money laundering and counter-terrorist financing measures. Financial Action Task Force. Retrieved from https://www.fatf-gafi.org/

[2] European Central Bank. (2022). Guide to fit and proper assessments. Retrieved from https://www.bankingsupervision.europa.eu

[3] Deloitte. (2021). Regulatory consent orders: Responding with resilience. Deloitte Insights. Retrieved from https://www2.deloitte.com

[4] Wang, X., Lin, X., & Song, Y. (2021). Artificial intelligence applications in anti-money laundering. Journal of Financial Regulation and Compliance, 29(4), 527–540.

[5] Kou, G., Chao, X., Peng, Y., Alsaadi, F. E., & Herrera-Viedma, E. (2021). Machine learning methods for systemic risk analysis in financial sectors. Technological Forecasting and Social Change, 163, 120481.

[6] Leins, K., & Crawford, K. (2020). What does it mean to govern AI? Nature Machine Intelligence, 2(7), 426–427.

[7] Avin, S., Belfield, H., Brundage, M., Krueger, G., Wang, J., Weller, A., Anderljung, M., Krawczuk, I., Krueger, D., Lebensold, J., Maharaj, T., & Zilberman, N. (2021). Filling gaps in trustworthy development of AI. Science, 374(6573), 1327–1329.

[8] Smith, J., & Banerjee, R. (2023). A review of machine learning techniques for AML. Journal of Financial Intelligence, 17(2), 144–160.

[9] Lin, K., & Omar, F. (2022). AI-enabled KYC for digital banks. Banking Technology Today, 28(4), 201–218.

[10] Huang, L., & Williams, C. (2021). Explainable AI in financial regulation: A compliance perspective. AI & Society, 36(3), 355–370.

[11] Gomez, T., & Shah, M. (2020). Detecting suspicious transactions with deep learning. Journal of Financial Crime, 27(4), 985–998.

[12] Leins, K., & Crawford, K. (2020). Governance frameworks for AI in financial services. Nature Machine Intelligence, 2(7), 426–427.

[13] Ahmed, S., & Krishnan, R. (2019). Natural language processing for AML alerts. Information

Systems Frontiers, 21(4), 867–881.

[14] Zhao, Y., & Mehta, S. (2019). AI for fraud detection in credit card issuance: A case study. Journal of Banking Regulation, 20(3), 278–295.

[15] Deloitte. (2018). AML automation: Balancing compliance and innovation. Deloitte Financial Insights Report. Retrieved from https://www2.deloitte.com

[16] Tran, D., & Hall, E. (2017). Data quality and risk in AML systems. Journal of Compliance Analytics, 12(2), 95–110.

[17] Kumar, P., & Sinha, D. (2016). Machine learning for risk-based KYC. AI in Banking Review, 8(1), 56–70.

[18] Fatemi, A. M., & Daryaei, A. A. (2023). Data governance and AI-based compliance in digital banking. Journal of Financial Technology, 14(1), 45–61.

[19] Bhat, R., & Singh, T. (2022). Intelligent KYC: Applying AI in customer verification. Journal of AI & Data Ethics, 3(4), 210–225.

[20] Gomez, T., & Shah, M. (2020). Detecting suspicious transactions with deep learning. Journal of Financial Crime, 27(4), 985–998.

[21] Huang, L., & Williams, C. (2021). Explainable AI in financial regulation: A compliance perspective. AI & Society, 36(3), 355–370.

[22] Deloitte. (2021). Regulatory consent orders: Responding with resilience. Deloitte Insights. Retrieved from https://www2.deloitte.com

[23] Adekunle, B. (2025). A unified compliance operations framework integrating AML, ESG, and transaction monitoring standards. International Journal of Multidisciplinary Research and Growth Evaluation. Advance online publication. https://doi.org/10.54660/IJMRGE.2022.3.2.639‑649

[24] The new EU Authority for Anti‑Money Laundering and Countering the Financing of Terrorism (AMLA): Legal and institutional innovations. Studies in Conflict & Terrorism. Advance online publication. https://doi.org/10.1080/1057610X.2025.2460594

[25] Redman, T. C. (2021). Data quality in the age of AI: Managing enterprise data at scale. Harvard Business Review, 99(3), 88–94.

[26] Kou, G., Peng, Y., & Chen, Y. (2021). Financial crime analytics using AI and ML techniques: A survey. Technological Forecasting and Social

Change, 163, 120481.

[27] From Trustworthy Principles to a Trustworthy Development Process: The Need and Elements of Trusted Development of AI Systems. AI, 4(4), 904–925. https://doi.org/10.3390/ai4040046

[28] Accenture. (2022). From reaction to prevention: AI in financial crime compliance. Accenture Finance & Risk Research. Retrieved from https://www.accenture.com

[29] Wang, L., Zhu, Y., & Huang, J. (2022). Machine learning approaches for AML in credit risk evaluation. Journal of Financial Data Science, 4(2), 101–117.

[30] Singh, R., & Lee, D. (2022). Benchmarking AI models in anti-fraud financial applications. AI in Finance Review, 15(1), 45–62.

[31] KPMG. (2023). AI in compliance: Enhancing AML operations and risk management. KPMG Risk & Compliance Series. Retrieved from https://home.kpmg/

[32] Accenture. (2022). From reaction to prevention: AI in financial crime compliance. Accenture Finance & Risk Research. Retrieved from https://www.accenture.com

[33] Huang, L., & Williams, C. (2021). Explainable AI in financial regulation: A compliance perspective. AI & Society, 36(3), 355–370.

[34] Basel Committee on Banking Supervision. (2021). Principles for effective management and supervision of climate-related financial risks. Retrieved from https://www.bis.org/

[35] Deloitte. (2021). Regulatory consent orders: Responding with resilience. Deloitte Insights. Retrieved from https://www2.deloitte.com

[36] Financial Conduct Authority. (2022). Guidance on the use of AI in financial services. Retrieved from https://www.fca.org.uk

[37] Chander, B., John, C., Warrier, L., & Gopalakrishnan, K. (2024). Toward trust‑worthy artificial intelligence (TAI) in the context of explainability and robustness. ACM Computing Surveys. Advance online publication. https://doi.org/10.1145/3675392

[38] Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology, 10(2), 1–19.

[39] Moin, A., & Qamar, F. (2021). Blockchain-

powered AI for AML compliance: A roadmap. Journal of Digital Trust, 3(1), 44–61.

[40] Rahman, A., & James, K. (2020). Collaborative AI for cross-border AML threat detection. International Journal of Financial Crime, 27(2), 325–342.

[41] Abbas, A., & Bhuiyan, M. Z. A. (2022). AI-enabled risk-based KYC systems for financial institutions. Journal of Applied AI & Finance, 5(1), 65–82.

[42] IEEE Standards Association. (2023). Ethical Assurance of Machine Learning Systems. IEEE P7003 Working Group Report.

[43] Binns, R., Veale, M., Van Kleek, M., & Shadbolt, N. (2018). ‘It's reducing a human being to a percentage’: Perceptions of justice in algorithmic decisions. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1–14s

Downloads

Published

2025-03-30

How to Cite

Sanjay Chandrakant Vichare. (2025). AI-Driven Data Governance for AML/KYC in Credit Card Issuance: A Framework to Reduce Regulatory Consent Orders. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.3757

Issue

Section

Research Article