Improving client risk classification with machine learning to increase anti-money laundering detection efficiency

IF 1.3 Q3 CRIMINOLOGY & PENOLOGY
Endre Jo Reite, Johan Karlsen, Elias Grefstad Westgaard
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引用次数: 0

Abstract

Purpose

This study aims to describe and empirically explore a new method for bank anti-money laundering (AML) systems using machine learning models. Current automated money laundering detection systems are notorious for flagging many false positives, causing bank employees to spend unnecessary time manually checking transactions that do not constitute money laundering. Decreasing the number of false positives can free up resources for investigating money laundering.

Design/methodology/approach

This study uses unique bank data on small- and medium-sized enterprises (SMEs) to examine how various client risk classification models can predict future suspicious transactions. This study explores various sources of client risk data and machine-learning approaches.

Findings

Client risk classification models can accurately predict suspicious future transactions. Adding accounting data and credit score information to client risk classification dramatically improves accuracy. This makes it easier to balance the risk of missing suspicious transactions with the need to reduce the number of false positives.

Practical implications

The suggested approach with readily available data sources and a focus on classifying client risk in a dynamic model can help banks significantly improve their efficiency by targeting their AML efforts toward the riskiest clients.

Originality/value

To the best of the authors’ knowledge, this study is the first to empirically explore machine learning in client risk classification, document how machine learning in client risk classification can significantly reduce false positives by incorporating novel, but readily available sources, such as credit risk and accounting data.

利用机器学习改进客户风险分类,提高反洗钱检测效率
目的 本研究旨在描述和实证探索一种使用机器学习模型的银行反洗钱(AML)系统新方法。当前的反洗钱自动检测系统因标记出许多误报而臭名昭著,导致银行员工花费不必要的时间手动检查不构成洗钱的交易。本研究使用中小型企业(SMEs)的独特银行数据来研究各种客户风险分类模型如何预测未来的可疑交易。本研究探讨了客户风险数据的各种来源和机器学习方法。研究结果客户风险分类模型可以准确预测未来的可疑交易。在客户风险分类中加入会计数据和信用评分信息可显著提高准确性。实际意义所建议的方法具有现成的数据源,并侧重于在动态模型中对客户风险进行分类,可以帮助银行将反洗钱工作的目标锁定在风险最高的客户身上,从而显著提高效率。独创性/价值 据作者所知,本研究首次对客户风险分类中的机器学习进行了实证探索,并记录了客户风险分类中的机器学习如何通过纳入新颖但随时可用的数据源(如信用风险和会计数据)来显著减少误报。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Money Laundering Control
Journal of Money Laundering Control CRIMINOLOGY & PENOLOGY-
CiteScore
2.70
自引率
27.30%
发文量
59
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