Current Status, Existino Issues, and OptimizationSuggestions for AgriculturalBank of China's Anti-Money Laundering Operations
Journal: Modern Economics & Management Forum DOI: 10.32629/memf.v6i6.4632
Abstract
This article focuses on the anti money laundering business of Agricultural Bank of China. Firstly, it reviews its development status. The anti money laundering organizational structure has gone through three stages of extensive management, centralized management, and refined management, forming a hierarchical system coordinated by the anti money laundering leadership group and coordinated by multiple departments. The main work is carried out around the three core areas of customer identity recognition, transaction reporting, and risk assessment, and a clear division of labor supervision and scientific assessment and evaluation system is constructed. Continuing to analyze the root causes of the problem, it is pointed out that in the context of unprecedented regulatory requirements, Agricultural Bank of China has problems such as insufficient manpower allocation, lack of standardization in work processes, and lagging behind in the construction of anti money laundering systems compared to business development.
Keywords
Agricultural Bank of China; anti money laundering; customer due diligence; intelligent monitoring system
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[1] Wibowo I G, Setijaningsih H T.Navigating Compliance: Regulatory Technology's Role in Anti-Money Laundering in Indonesia Banking[J].Dinasti International Journal of Economics, Finance & Accounting (DIJEFA), 2024, 5(4): 46-54.
[2] Minto A.Certainties and Uncertainties Surrounding Central Bank Digital Currencies (CBDC) vis-à-vis the EU Anti-Money Laundering Regulatory Framework[J].European Company & Financial Law Review, 2024, 21(5/6): 671-677.
[3] Ferretti S, D'Angelo G, Ghini V.Enhancing Anti-Money Laundering Frameworks: An Application of Graph Neural Networks in Cryptocurrency Transaction Classification[J].IEEE Access, 2025, 13: 50221-20215.
[4] Nicknora A T.Anti-Money Laundering Compliance and Financial Performance of Selected Commercial Banks in South Sudan: A Survey of Literature on Compliance Audits[J].Open Journal of Business and Management, 2024, 12(05): 3543-3556.
[5] Effendi F, Chattopadhyay A.Privacy-Preserving Graph-Based Machine Learning with Fully Homomorphic Encryption for Collaborative Anti-Money Laundering[J].IEEE, 2024, 8(87): 85-95.
[2] Minto A.Certainties and Uncertainties Surrounding Central Bank Digital Currencies (CBDC) vis-à-vis the EU Anti-Money Laundering Regulatory Framework[J].European Company & Financial Law Review, 2024, 21(5/6): 671-677.
[3] Ferretti S, D'Angelo G, Ghini V.Enhancing Anti-Money Laundering Frameworks: An Application of Graph Neural Networks in Cryptocurrency Transaction Classification[J].IEEE Access, 2025, 13: 50221-20215.
[4] Nicknora A T.Anti-Money Laundering Compliance and Financial Performance of Selected Commercial Banks in South Sudan: A Survey of Literature on Compliance Audits[J].Open Journal of Business and Management, 2024, 12(05): 3543-3556.
[5] Effendi F, Chattopadhyay A.Privacy-Preserving Graph-Based Machine Learning with Fully Homomorphic Encryption for Collaborative Anti-Money Laundering[J].IEEE, 2024, 8(87): 85-95.
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