Ai-Enabled Family-Linked Credit Intelligence For Global Digital Banking Platforms: A Strategy, Analytics, And Agentic Governance Framework

Sriram Polavakalipalayam Ramakrishnan

doi:10.29284/ee6dqs36

Authors

Sriram Polavakalipalayam Ramakrishnan Thayer School of Engineering at Dartmouth and Tuck School of Business at Dartmouth, Dartmouth College, Hanover, NH, USA, ORCID: 0009-0002-2319-3194

DOI:

https://doi.org/10.29284/ee6dqs36

Keywords:

agentic AI, AI underwriting, Azure cloud, credit unions, cross-border finance, family linkage, fraud graphs, IFRS 9, metaheuristics, model risk management, reinforcement learning.

Abstract

This academic research paper develops the Family-Linked Credit Intelligence (FLCI) framework, a conceptual contribution at the intersection of financial technology, household economics, and responsible AI. Motivated by the goal of strengthening family financial bonds through shared, consented, and transparent financial visibility, FLCI examines how federally insured credit unions operating under NCUA supervision can better serve internationally mobile members whose cross-border financial histories are often invisible to domestic bureaus. The framework integrates consented, verified immediate-family signals into an explainable analytics stack designed to align with Regulation P, Regulation B, SR 11-7-informed model risk governance, and the voluntary NIST AI Risk Management Framework. The work is purely conceptual and mathematically grounded; no member-level or proprietary data is used, and the study is conducted as an independent research contribution intended to complement, not substitute for, regulator-led supervisory judgment. We specify cloud-native MLOps with feature stores, champion/challenger governance, drift and fairness monitoring, and graph-based fraud detection. A non-authoritative agentic layer generates audit-ready artifacts through tool-using LLMs under human approval. Under stated assumptions, illustrative scenario analysis suggests potential membership expansion of 5 to 14% and potential incremental annual revenue of roughly $15 to 38 million, alongside a research-level hypothesis that transparent family-shared financial visibility may foster stronger household financial bonds, subject to pilot validation.

References

[1] National Credit Union Administration, “Privacy of consumer financial information (regulation p),” Federal Consumer Financial Protection Guide, 2023, https://ncua.gov/regulation-supervision/manuals-guides/federal-consumer-financial-protection-guide.

[2] IFRS Foundation, “IFRS 9 financial instruments,” International Financial Reporting Standards, 2014, https://www.ifrs.org/issued-standards/list-of-standards/ifrs-9-financial-instruments/.

[3] CFPB, “Regulation B (equal credit opportunity act): 12 CFR part 1002,” Consumer Financial Protection Bureau, 2023, https://www.consumerfinance.gov/rules-policy/regulations/1002/.

[4] Federal Reserve, “SR 11-7: Guidance on model risk management,” Supervisory and Regulation Letters, 2011,

https://www.federalreserve.gov/supervisionreg/srletters/sr1107.htm.

[5] Microsoft, “Azure well-architected framework,” Microsoft Learn, 2024, https://learn.microsoft.com/azure/well-architected/.

[6] Databricks, “Delta lake documentation,” Delta Lake Project, 2024, https://docs.delta.io/.

[7] L. Akoglu, H. Tong, and D. Koutra, “Graph based anomaly detection and description: A survey,” Data Mining and Knowledge Discovery, vol. 29, no. 3, pp. 626–688, 2015.

[8] Z. Chen et al., “Parallel path detection for fraudulent accounts in banks based on graph analysis,” PeerJ Computer Science, vol. 9, p. e1268, 2023.

[9] National Credit Union Administration, “Quarterly credit union data summary, q4 2023,” NCUA, 2024, https://ncua.gov/files/publications/analysis/quarterly-data-summary-2023-Q4.pdf.

[10] FTC, “Standards for safeguarding customer information (safeguards rule),” Federal Trade Commission, 2023, https://www.ftc.gov/business-guidance/resources/ftc-safeguards-rule-what-your-business-needs-know.

[11] National Credit Union Administration, “Evaluating third party relationships (07-cu-13),” Letter to Credit Unions, 2007, https://ncua.gov/regulation-supervision/letters-credit-unions-other-guidance/evaluating-third-party-relationships.

[12] Basel Committee on Banking Supervision, “Principles for the sound management of third-party risk,” BIS, Dec. 2025, https://www.bis.org/bcbs/publ/d605.htm.

[13] European Commission, “Digital operational resilience act (DORA): Regulation (EU) 2022/2554,” European Commission Finance: Digital Operational Resilience Regulation, 2022,https://finance.ec.europa.eu/regulation-and-supervision/financial-services-legislation/implementing-and-delegated-acts/digital-operational-resilience-regulation_en.

[14] European Banking Authority, “Guidelines amending guidelines EBA/GL/2019/04 on ICT and security risk management (EBA/GL/2025/02),” EBA Guidelines, applicable from 20 May 2025, 2025, https://www.eba.europa.eu/regulation-and-policy/internal-governance/guidelines-on-ict-and-security-risk-management.

[15] S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in Advances in Neural Information Processing Systems (NeurIPS), 2017, pp. 4765–4774.

[16] M. Hardt, E. Price, and N. Srebro, “Equality of opportunity in supervised learning,” in Advances in Neural Information Processing Systems (NeurIPS), 2016, pp. 3315–3323.

[17] S. Barocas, M. Hardt, and A. Narayanan, Fairness and Machine Learning: Limitations and Opportunities.MIT Press, 2023, https://fairmlbook.org/.

[18] ISO/IEC, “ISO/IEC 27001: Information security management systems,” International Organization for Standardization, 2022, https://www.iso.org/isoiec-27001-information-security.html.

[19] OWASP, “Application security verification standard (ASVS),” OWASP Foundation, 2023, https://owasp.org/www-project-application-security-verification-standard/.

[20] National Institute of Standards and Technology, “Cybersecurity framework (CSF) 2.0,” National Institute of Standards and Technology, Tech. Rep., 2024, https://www.nist.gov/cyberframework.

[21] NIST, “AI risk management framework (AI RMF 1.0),” National Institute of Standards and Technology, Tech. Rep., 2023, https://www.nist.gov/itl/ai-risk-management-framework.

[22] Microsoft Corporation, “Azure API management documentation,” Microsoft Learn, 2024, https://learn.microsoft.com/azure/api-management/.

[23] R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, 2nd ed.MIT Press, 2018.

[24] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, 2002.

[25] T. Schick, J. Dwivedi-Yu, R. Dessì, R. Raileanu, M. Lomeli, L. Zettlemoyer, N. Cancedda, and T. Scialom, “Toolformer: Language models can teach themselves to use tools,” in Advances in Neural Information Processing Systems (NeurIPS), 2023, https://arxiv.org/abs/2302.04761.

[26] Microsoft Databricks, “Azure databricks documentation,” Microsoft Learn, 2024, https://learn.microsoft.com/azure/databricks/.

[27] S. Yao et al., “ReAct: Synergizing reasoning and acting in language models,” arXiv preprint arXiv:2210.03629, 2022.

[28] Microsoft Azure, “Azure machine learning documentation,” Microsoft Learn, 2024, https://learn.microsoft.com/azure/machine-learning/.

[29] National Credit Union Administration, “Credit union system performance data and industry trends,” NCUA Analysis Center, 2025, https://www.ncua.gov/analysis/credit-union-corporate-call-report-data.

[30] J. Dew, S. Britt, and S. Huston, “Examining the relationship between financial issues and divorce,” Family Relations, vol. 61, no. 4, pp. 615–628, 2012, https://scholarsarchive.byu.edu/facpub/4526/.

[31] National Credit Union Administration, “MyCreditUnion.gov: Consumer financial protection resource,” NCUA Consumer Education, 2025, https://mycreditunion.gov/.

[32] National Credit Union Administration, “NCUA credit union locator: Federally insured credit unions,” NCUA Mapping Service, 2025, https://mapping.ncua.gov/ResearchCreditUnion.

[33] National Credit Union Administration, “National credit union share insurance fund (NCUSIF),” NCUA Support Services, 2025, https://ncua.gov/support-services/share-insurance-fund.

[34] National Credit Union Administration, “CUonline: Profile and call report submission service,” NCUA Regulatory Reporting, 2025, https://ncua.gov/regulation-supervision/regulatory-reporting/cuonline.

[35] National Credit Union Administration, “Financial performance reports (FPR) for federally insured credit unions,” NCUA Analysis, 2025, https://ncua.gov/analysis/credit-union-corporate-call-report-data/financial-performance-reports.

[36] National Credit Union Administration, “NCUA administrative orders and enforcement actions,” NCUA Enforcement, 2025, https://ncua.gov/regulation-supervision/enforcement-actions/administrative-orders.

[37] National Credit Union Administration, “NCUA cybersecurity resources program,” NCUA, 2025, https://ncua.gov/cybersecurity.

[38] National Credit Union Administration, “NCUA’s 2026 supervisory priorities,” Letter to Credit Unions 26-CU-01, January 2026, 2026, https://ncua.gov/regulation-supervision/letters-credit-unions-other-guidance/ncuas-2026-supervisory-priorities.

[39] National Credit Union Administration, “Removal of disparate impact from supervisory practice,” Letter to Credit Unions, September 2025, 2025, https://ncua.gov/regulation-supervision/letters-credit-unions-other-guidance/removal-disparate-impact.

[40] National Credit Union Administration, “Elimination of reputation risk from supervisory examinations,” Letter to Credit Unions, September 2025, 2025, https://ncua.gov/regulation-supervision/letters-credit-unions-other-guidance/elimination-reputation-risk.