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Machine learning approaches for constructing the national anti-money laundering index

Author

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  • Zhang, Guike
  • Gao, Zengan
  • Dong, June
  • Mei, Dexiang

Abstract

This paper proposes a methodology for constructing the national anti-money laundering (AML) index based on Mutual Evaluation reports and machine learning models. We employ LASSO and random forests to jointly identify the key factors affecting AML, which have policy implications for regulatory authorities to optimize the allocation of AML resources. The random forests five-factor (RF-FF) model proposed in this paper has high prediction accuracy (86.31%) and good out-of-sample predictive ability for the MER-AML index, which is significantly better than competing models such as OLS and relaxed LASSO. The time-series national AML index constructed based on the RF-FF model contributes to overcoming the limitations of existing methods, providing fresh perspectives on the measurement of AML systems, and facilitating empirical studies related to evaluating the controversial AML regime.

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  • Zhang, Guike & Gao, Zengan & Dong, June & Mei, Dexiang, 2023. "Machine learning approaches for constructing the national anti-money laundering index," Finance Research Letters, Elsevier, vol. 52(C).
    • Handle: RePEc:eee:finlet:v:52:y:2023:i:c:s1544612322007449
    DOI: 10.1016/j.frl.2022.103568
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    2. Chen, Dangxing & Ye, Jiahui & Ye, Weicheng, 2023. "Interpretable selective learning in credit risk," Research in International Business and Finance, Elsevier, vol. 65(C).

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    JEL classification:

    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • F42 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - International Policy Coordination and Transmission
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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