Correspondent Banking and Nested Relationships: Managing Cross-Border AML/CTF Risk, Due Diligence, and Monitoring Expectations

Authors

  • Mallikarjun Reddy Gouni University of Illinois Springfield. Author

DOI:

https://doi.org/10.63282/3050-9246.IJETCSIT-V5I1P116

Keywords:

Correspondent Banking, Nested Relationships, Cross-Border Banking, International Financial Transactions, Foreign Correspondent Accounts, Payable-Through Accounts, Intermediary Banking, Anti-Money Laundering (AML), Counter-Terrorist Financing (CTF), Financial Crime Compliance, Regulatory Compliance

Abstract

Correspondent banking remains a critical backbone of cross-border payments, trade finance, and financial inclusion, yet it is also a persistent exposure point for money laundering and terrorist financing (AML/CTF) risk. These risks intensify in nested correspondent relationships where respondent banks provide downstream access to their own clients or other financial institutions because transparency diminishes across the payment chain and accountability for controls can become fragmented. This study examines how financial institutions can manage AML/CTF risk in correspondent and nested relationships while meeting evolving expectations for risk-based due diligence, transaction monitoring, and ongoing oversight across multiple jurisdictions. The research synthesizes regulatory guidance, supervisory enforcement themes, and industry practices to develop a structured framework for (i) risk scoping and customer/relationship classification, (ii) enhanced due diligence (EDD) for nested access, including governance, ownership, sanctions exposure, and AML program effectiveness, and (iii) monitoring and escalation standards that integrate payment-message data quality, typology-driven alerts, and periodic relationship reviews. Methodologically, the study employs comparative policy analysis across key regulatory regimes and a scenario-based assessment using representative nested-relationship typologies (e.g., indirect access to high-risk geographies, fintech/payment intermediary layering, and payable-through account–like structures) to identify practical control gaps and feasible mitigations. The expected contribution is a set of actionable, risk-proportionate recommendations and monitoring expectations that balance financial crime compliance with the operational realities of cross-border banking, supporting safer access to the global financial system without unnecessary de-risking.

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References

[1] AUSTRAC. (2024, January 23). Due diligence of correspondent banking relationships. AUSTRAC. https://www.austrac.gov.au/due-diligence-correspondent-banking-relationships

[2] Bank for International Settlements, Basel Committee on Banking Supervision. (2009). Due diligence and transparency regarding cover payment messages related to cross-border wire transfers (BCBS 154). https://www.bis.org/publ/bcbs154.pdf

[3] Basel Committee on Banking Supervision. (2017). Revised annex on correspondent banking (BCBS d389). Bank for International Settlements. https://www.bis.org/bcbs/publ/d389.pdf

[4] Basel Committee on Banking Supervision. (2020). Sound management of risks related to money laundering and financing of terrorism (BCBS d505). Bank for International Settlements. https://www.bis.org/bcbs/publ/d505.pdf

[5] Board of Governors of the Federal Reserve System. (2023). Due diligence programs for correspondent accounts for foreign financial institutions: Examination procedures (SR 23-6 / CA 23-4).

[6] Vattikonda, N., Gupta, A. K., Polu, A. R., Narra, B., Buddula, D. V. K. R., & Patchipulusu, H. H. S. (2022). Blockchain Technology in Supply Chain and Logistics: A Comprehensive Review of Applications, Challenges, and Innovations. International Journal of Emerging Trends in Computer Science and Information Technology, 3(3), 72-80.

[7] Attipalli, A., BITKURI, V., Mamidala, J. V., Kendyala, R., & KURMA, J. (2022). Empowering Cloud Security with Artificial Intelligence: Detecting Threats Using Advanced Machine learning Technologies. Available at SSRN 5741263.

[8] Routhu, K. K. (2022). From RFID to Geofencing: IoT-Enabled Smart Time Tracking in Oracle HCM Cloud. International Journal of Science, Engineering and Technology, 10(4).

[9] Polam, R. M., Kamarthapu, B., Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., & Vangala, S. R. (2022). Data Security in Cloud Computing: Encryption, Zero Trust, and Homomorphic Encryption. International Journal of Emerging Trends in Computer Science and Information Technology, 3(4), 31-41.

[10] Routhu, K. K. (2022). From Case Management to Conversational HR: Redefining Help Desks with Oracle’s AI and NLP Framework. International Journal of Science, Engineering and Technology, 10(6).

[11] https://www.federalreserve.gov/supervisionreg/srletters/SR2306a3.pdf

[12] Borchert, L., De Haas, R., Kirschenmann, K., & Schultz, A. (2023). Broken relationships: De-risking by correspondent banks and international trade (EBRD Working Paper No. 285). https://doi.org/10.2139/ssrn.4658618

[13] Committee on Payments and Market Infrastructures. (2016). Correspondent banking (CPMI d147). Bank for International Settlements. https://www.bis.org/cpmi/publ/d147.pdf

[14] Committee on Payments and Market Infrastructures. (2020). Enhancing cross-border payments: Building blocks of a global roadmap (CPMI d193). Bank for International Settlements. https://www.bis.org/cpmi/publ/d193.pdf

[15] Bank for International Settlements, International Monetary Fund, & World Bank. (2023). Exploring multilateral platforms for cross-border payments (Analytical Notes No. 2023/001). International Monetary Fund. https://doi.org/10.5089/9798400227363.064

[16] Mamidala, J. V., Enokkaren, S. J., Attipalli, A., Bitkuri, V., Kendyala, R., & Kurma, J. (2023). Machine Learning Models Powered by Big Data for Health Insurance Expense Forecasting. International Research Journal of Economics and Management Studies IRJEMS, 2(1).

[17] Bitkuri, V., Kendyala, R., Kurma, J., Enokkaren, S. J., & Mamidala, J. V. (2023). Forecasting Stock Price Movements With Deep Learning Models for time Series Data Analysis. Journal of Artificial Intelligence & Cloud Computing. SRC/JAICC-531. DOI: doi. org/10.47363/JAICC/2023 (2), 489, 2-9.

[18] Singh, A. A. S. S., Mania, V., Kothamaram, R. R., Rajendran, D., Namburi, V. D. N., & Tamilmani, V. (2023). Exploration of Java-Based Big Data Frameworks: Architecture, Challenges, and Opportunities. Journal of Artificial Intelligence & Cloud Computing, 2(4), 1-8.

[19] Routhu, K. K. (2023). AI-driven succession planning in Oracle HCM Cloud: Building resilient leadership pipelines through predictive analytics. International Journal of Science, Engineering and Technology, 11(5).

[20] Tamilmani, V., Namburi, V. D., Singh Singh, A. A., Maniar, V., Kothamaram, R. R., & Rajendran, D. (2023). Real-Time Identification of Phishing Websites Using Advanced Machine Learning Methods. Available at SSRN 5837142.

[21] From Fragmentation to Focus: The Benefits of Centralizing Procurement. (2023). International Journal of Research and Applied Innovations, 6(6), 9820-9833. https://doi.org/10.15662/

[22] Routhu, K. K. (2023). Embedding fairness into the digital enterprise, data driven DEI strategies with Oracle HCM Analytics. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 9(8), 266-274.

[23] Routhu, K. K. (2023). AI-driven skills forecasting in Oracle HCM Cloud: From static competencies to predictive workforce design. International Journal of Science, Engineering and Technology, 11(1).

[24] European Banking Authority. (2023). Guidelines on money laundering and terrorist financing risk factors, including simplified and enhanced customer due diligence, and the factors credit and financial institutions should consider when assessing the money laundering and terrorist financing risk associated with individual business relationships and occasional transactions (EBA/GL/2023/03). https://www.eba.europa.eu/sites/default/files/2023-05/EBA%20GL%202023%2003%20-%20Final%20Report%20on%20ML_TF%20risk%20factors.pdf

[25] Europol. (2023). Internet organised crime threat assessment (IOCTA) 2023. Europol. https://www.europol.europa.eu/publication-events/main-reports/internet-organised-crime-threat-assessment-iocta-2023

[26] Financial Action Task Force. (2014, October). Guidance for a risk-based approach: The banking sector. FATF. https://www.fatf-gafi.org/content/dam/fatf-gafi/guidance/Risk-Based-Approach-Banking-Sector.pdf.coredownload.pdf

[27] Financial Action Task Force. (2016). Guidance on correspondent banking services. FATF. https://www.fatf-gafi.org/content/dam/fatf/documents/reports/Guidance-Correspondent-Banking-Services.pdf

[28] Financial Action Task Force. (2012). International standards on combating money laundering and the financing of terrorism & proliferation: The FATF recommendations. FATF. https://www.fatf-gafi.org/content/dam/fatf-gafi/recommendations/FATF%20Recommendations%202012.pdf.coredownload.inline.pdf

[29] Polu, A. R., Buddula, D. V. K. R., Narra, B., Gupta, A., Vattikonda, N., & Patchipulusu, H. (2021). Evolution of AI in Software Development and Cybersecurity: Unifying Automation, Innovation, and Protection in the Digital Age. Available at SSRN 5266517.

[30] Bitkuri, V., Kendyala, R., Kurma, J., Mamidala, V., Enokkaren, S. J., & Attipalli, A. (2021). Systematic Review of Artificial Intelligence Techniques for Enhancing Financial Reporting and Regulatory Compliance. International Journal of Emerging Trends in Computer Science and Information Technology, 2(4), 73-80.

[31] Attipalli, A., Enokkaren, S., BITKURI, V., Kendyala, R., KURMA, J., & Mamidala, J. V. (2021). Enhancing Cloud Infrastructure Security Through AI-Powered Big Data Anomaly Detection. Available at SSRN 5741305.

[32] Singh, A. A. S., Tamilmani, V., Maniar, V., Kothamaram, R. R., Rajendran, D., & Namburi, V. D. (2021). Predictive Modeling for Classification of SMS Spam Using NLP and ML Techniques. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(4), 60-69.

[33] Kothamaram, R. R., Rajendran, D., Namburi, V. D., Singh, A. A. S., Tamilmani, V., & Maniar, V. (2021). A Survey of Adoption Challenges and Barriers in Implementing Digital Payroll Management Systems in Across Organizations. International Journal of Emerging Research in Engineering and Technology, 2(2), 64-72.

[34] Rajendran, D., Namburi, V. D., Singh, A. A. S., Tamilmani, V., Maniar, V., & Kothamaram, R. R. (2021). Anomaly Identification in IoT-Networks Using Artificial Intelligence-Based Data-Driven Techniques in Cloud Environmen. International Journal of Emerging Trends in Computer Science and Information Technology, 2(2), 83-91.

[35] Attipalli, A., BITKURI, V., KURMA, J., Enokkaren, S., Kendyala, R., & Mamidala, J. V. (2021). A Survey of Artificial Intelligence Methods in Liquidity Risk Management: Challenges and Future Directions. Available at SSRN 5741342.

[36] Routhu, K. K. (2021). AI-augmented benefits administration: A standards-driven automation framework with Oracle HCM Cloud. International Journal of Scientific Research and Engineering Trends, 7(3).

[37] Routhu, K. K. (2021). Harnessing AI Dashboards in Oracle Cloud HCM: Advancing Predictive Workforce Intelligence and Managerial Agility. International Journal of Scientific Research & Engineering Trends, 7(6).

[38] Financial Stability Board. (2016, December 19). FSB action plan to assess and address the decline in correspondent banking. https://www.fsb.org/2016/12/fsb-action-plan-to-assess-and-address-the-decline-in-correspondent-banking/

[39] Financial Stability Board. (2019, May 29). FSB action plan to assess and address the decline in correspondent banking: 2019 progress report (P290519-1). https://www.fsb.org/uploads/P290519-1.pdf

[40] Financial Stability Board. (2020, October 13). Enhancing cross-border payments: Stage 3 roadmap (P131020-1). https://www.fsb.org/uploads/P131020-1.pdf

[41] Association of Certified Fraud Examiners. (2022). Occupational fraud 2022: A report to the nations on occupational fraud and abuse. Association of Certified Fraud Examiners. https://www.acfe.com/report-to-the-nations/2022/

[42] Kranthi Kumar Routhu. (2020). Intelligent Remote Workforce Management: AI, Integration, and Security Strategies Using Oracle HCM Cloud. KOS Journal of AIML, Data Science, and Robotics, 1(1), 1–5. https://doi.org/10.5281/zenodo.17531257

[43] Routhu, K. K. (2020). Strategic Compensation Equity and Rewards Optimization: A Multi-cloud Analytics Blueprint with Oracle Analytics Cloud. Available at SSRN 5737266.

[44] International Monetary Fund. (2016, June). The withdrawal of correspondent banking relationships: A case for policy action (Staff Discussion Note SDN/16/06). https://www.imf.org/external/pubs/ft/sdn/2016/sdn1606.pdf

[45] Office of Foreign Assets Control. (2022, September 30). Sanctions compliance guidance for instant payment systems. U.S. Department of the Treasury. https://ofac.treasury.gov/system/files/126/instant_payment_systems_compliance_guidance_brochure.pdf

[46] Wolfsberg Group. (2023). Correspondent Banking Due Diligence Questionnaire (CBDDQ) (Version 1.4) [Resource page]. https://wolfsberg-group.org/resources/

[47] Routhu, K. K. (2019). Hybrid machine learning architecture for absence forecasting within Oracle Cloud HCM. KOS Journal of AIML, Data Science, and Robotics, 1(1), 1-5.

[48] Routhu, K. K. (2019). Conversational AI in Human Capital Management: Transforming Self-Service Experiences with Oracle Digital Assistant. International Journal of Scientific Research & Engineering Trends, 5(6).

[49] Wolfsberg Group. (2023, February 10). Publication of the CBDDQ, FCCQ, guidance, glossary and FAQs [News release]. https://wolfsberg-group.org/news/publication-of-the-cbddq-fccq-guidance-glossary-and-faqs/

[50] World Bank Group. (2017). The decline in access to correspondent banking services: Trends, impacts, and solutions. https://thedocs.worldbank.org/en/doc/786671524166274491-0290022018/render/TheDeclineinAccesstoCorrespondentBanking.pdf

[51] Routhu, K. K. (2019). AI-Enhanced Payroll Optimization: Improving Accuracy and Compliance in Oracle HCM. KOS Journal of AIML, Data Science, and Robotics, 1(1), 1-5.

[52] Routhu, K. K. (2018). Reusable Integration Frameworks in Oracle HCM: Accelerating Enterprise Automation through Standardized Architecture. International Journal of Scientific Research & Engineering Trends, 4(4).

Published

2024-03-30

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Section

Articles

How to Cite

1.
Reddy Gouni M. Correspondent Banking and Nested Relationships: Managing Cross-Border AML/CTF Risk, Due Diligence, and Monitoring Expectations. IJETCSIT [Internet]. 2024 Mar. 30 [cited 2026 Mar. 10];5(1):149-71. Available from: https://www.ijetcsit.org/index.php/ijetcsit/article/view/616

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