[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119390-en":3,"doc-seo-119390-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119390,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Improving client risk classification with machine learning to increase anti-money laundering detection efficiency","This study presents and empirically evaluates machine learning methods for bank anti-money laundering (AML) systems, targeting a key operational issue: excessive false positives that force costly manual transaction review. Using unique bank data on small- and medium-sized enterprises (SMEs), the research compares approaches to client risk classification and tests multiple sources of risk information. Results show accurate prediction of future suspicious transactions, with accounting data and credit scores substantially improving accuracy and helping balance missed-risk coverage against false-alarm reduction.","Emerald Master 0  \nImproving client risk classification with machine learning to  \nincrease anti-money laundering detection efficiency  \n\n| Journal:   | Journal of Money Laundering Control   |\n| --- | --- |\n| Manuscript ID   | JMLC-03-2024-0040.R1   |\n| Manuscript Type:   | Scholarly Article   |\n| Keywords:   | Money Laundering, Client Risk Classification, Machine learning,Supervised Learning, XGBoost   |\n\nPage 1 of 30 Emerald Master 0  \n1  \n2  \n3  \n4  \n5  \n6  \n7  \n8  \n9  \n10  \n11  \n12  \n13  \n14  \n15  \n16  \n17  \n18  \n19  \n20  \n21  \n22  \n23  \n24  \n25  \n26  \n27  \n28  \n29  \n30  \n31  \n32  \n33  \n34  \n35  \n36  \n37  \n38  \n39  \n40  \n41  \n42  \n43  \n44  \n45  \n46  \n47  \n48  \n49  \n50  \n51  \n52  \n53  \n54  \n55  \n56  \n57  \n58  \n59  \nImproving client risk classification with machine learning to increase anti-moneylaundering detection efficiency  \n# Abstract\n\nPurpose: This study describes and empirically explores a new method for bank anti-moneylaundering (AML) systems using machine learning models. Current automated moneylaundering detection systems are notorious for flagging many false positives, causing bankemployees to spend unnecessary time manually checking transactions that do not constitutemoney laundering. Decreasing the number of false positives can free up resources forinvestigating money laundering.  \nDesign/methodology: This study employs unique bank data on small- and medium-sizedenterprises (SMEs) to examine how various client risk classification models can predictfuture suspicious transactions. We explore various sources of client risk data and machinelearning approaches.  \nFindings: Client risk classification models can accurately predict suspicious futuretransactions. Adding accounting data and credit score information to client risk classificationdramatically improves accuracy. This makes it easier to balance the risk of missingsuspicious transactions with the need to reduce the number of false positives.  \nOriginality/value: This study is the first to empirically explore machine learning in clientrisk classification, document how machine learning in client risk classification cansignificantly reduce false positives by incorporating novel, but readily available sources, suchas credit risk and accounting data.  \nPractical implications: Our suggested approach with readily available data sources and afocus on classifying client risk in a dynamic model can help banks significantly improve theirefficiency by targeting their AML efforts toward the riskiest clients.  \n1  \n2  \n3  \n4  \n5  \n6  \n7  \n8  \n9  \n10  \n11  \n12  \n13  \n14  \n15  \n16  \n17  \n18  \n19  \n20  \n21  \n22  \n23  \n24  \n25  \n26  \n27  \n28  \n29  \n30  \n31  \n32  \n33  \n34  \n35  \n36  \n37  \n38  \n39  \n40  \n41  \n42  \n43  \n44  \n45  \n46  \n47  \n48  \n49  \n50  \n51  \n52  \n53  \n54  \n55  \n56  \n57  \n58  \n59  \nEmerald Master 0 Page 2 of 30  \n# Introduction\n\nMachine learning is increasingly used to detect money laundering activities to potentiallyidentify unusual financial behaviorsand patterns (Alotibi et al., 2022) . However, theliterature has limited research on this technique, indicating the need for further exploration inthis area (Zhang and Trubey, 2019). With the increasing complexity and speed of bankingtransactions, classifying client risk has become increasingly important in anti-moneylaundering (AML) efforts. The lack of access to high-quality training datasets (Canhoto,2021) and data quality issues are significant concerns leading to suboptimal machine learningmodels for money laundering detection (Gupta et al., 2022). Additionally, machine learningmodels return false positives, emphasizing the importance of minimizing such errors toensure accurate predictions (Ketenci et al., 2021). Moreover, the use of machine learningalgorithms to identify money laundering patterns and groups necessitates a critical review oftechniques to enhance detection effectiveness (Kute et al., 2021) .  \nFalse positives in AML detection are a significant challenge. Ketenci et al. (2021) emphasizethat most c","cbCailihiTCJ9DUF","https://ap.wps.com/l/cbCailihiTCJ9DUF","pdf",515440,1,23,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What problem does the study target in AML systems?\",\"answer\":\"The study targets the high rate of false positives in automated AML detection, which creates unnecessary manual review effort and can distract from genuinely suspicious activity.\"},{\"question\":\"What data and modeling approach does the study use?\",\"answer\":\"The study uses unique bank data on small- and medium-sized enterprises (SMEs) and evaluates client risk classification models based on different sources of client risk information and machine learning approaches.\"},{\"question\":\"Which additional data improves prediction accuracy?\",\"answer\":\"Adding accounting data and credit score information to client risk classification dramatically improves accuracy in predicting future suspicious transactions.\"}]","Improving client risk 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