Comparative analysis of machine learning algorithms for money laundering detection
Abstract This study explored the effectiveness of anomaly detection techniques in identifying fraudulent financial transactions, with a particular focus on money laundering activities. The research addressed the growing challenges of financial fraud, which significantly impacts economies and financi...
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| Format: | Article |
| Language: | English |
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Springer
2025-07-01
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| Series: | Discover Artificial Intelligence |
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| Online Access: | https://doi.org/10.1007/s44163-025-00397-4 |
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| author | Sunday Adeola Ajagbe Simphiwe Majola Pragasen Mudali |
| author_facet | Sunday Adeola Ajagbe Simphiwe Majola Pragasen Mudali |
| author_sort | Sunday Adeola Ajagbe |
| collection | DOAJ |
| description | Abstract This study explored the effectiveness of anomaly detection techniques in identifying fraudulent financial transactions, with a particular focus on money laundering activities. The research addressed the growing challenges of financial fraud, which significantly impacts economies and financial institutions by leading to substantial monetary losses and undermining trust in financial systems. This research examined contemporary machine learning (ML) algorithms, including XGBoost, K-Nearest Neighbors, Random Forest, Isolation Forest, and Support Vector Machines, to analyze transaction data for anomalies indicative of fraudulent behavior. This study’s approach involves data collecting, system design, implementation, data analysis, and experimental setup. This research aimed to identify robust algorithms for detecting financial fraud. In the results notably, XGBoost shows an output of 1.0, 1.0, 1.0, 1.0, and 0.94 for accuracy, precision, recall, F1 score and AUC respectively to outperform other ML algorithms experimented in money laundering detection. The findings underscore the potential of ML algorithms capability to combat money laundering and indeed enhance anti-money efforts and offer financial institutions a powerful, resource-efficient method for fraud detection in large-scale transaction environments. |
| format | Article |
| id | doaj-art-451560143e7d4ffeb3fe3c9c161c4902 |
| institution | Kabale University |
| issn | 2731-0809 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Artificial Intelligence |
| spelling | doaj-art-451560143e7d4ffeb3fe3c9c161c49022025-08-20T04:03:07ZengSpringerDiscover Artificial Intelligence2731-08092025-07-015111810.1007/s44163-025-00397-4Comparative analysis of machine learning algorithms for money laundering detectionSunday Adeola Ajagbe0Simphiwe Majola1Pragasen Mudali2Department of Computer Science, University of ZululandDepartment of Computer Science, University of ZululandDepartment of Computer Science, University of ZululandAbstract This study explored the effectiveness of anomaly detection techniques in identifying fraudulent financial transactions, with a particular focus on money laundering activities. The research addressed the growing challenges of financial fraud, which significantly impacts economies and financial institutions by leading to substantial monetary losses and undermining trust in financial systems. This research examined contemporary machine learning (ML) algorithms, including XGBoost, K-Nearest Neighbors, Random Forest, Isolation Forest, and Support Vector Machines, to analyze transaction data for anomalies indicative of fraudulent behavior. This study’s approach involves data collecting, system design, implementation, data analysis, and experimental setup. This research aimed to identify robust algorithms for detecting financial fraud. In the results notably, XGBoost shows an output of 1.0, 1.0, 1.0, 1.0, and 0.94 for accuracy, precision, recall, F1 score and AUC respectively to outperform other ML algorithms experimented in money laundering detection. The findings underscore the potential of ML algorithms capability to combat money laundering and indeed enhance anti-money efforts and offer financial institutions a powerful, resource-efficient method for fraud detection in large-scale transaction environments.https://doi.org/10.1007/s44163-025-00397-4Fraud detectionMoney launderingMachine learningAnomaly detectionXGBoost |
| spellingShingle | Sunday Adeola Ajagbe Simphiwe Majola Pragasen Mudali Comparative analysis of machine learning algorithms for money laundering detection Discover Artificial Intelligence Fraud detection Money laundering Machine learning Anomaly detection XGBoost |
| title | Comparative analysis of machine learning algorithms for money laundering detection |
| title_full | Comparative analysis of machine learning algorithms for money laundering detection |
| title_fullStr | Comparative analysis of machine learning algorithms for money laundering detection |
| title_full_unstemmed | Comparative analysis of machine learning algorithms for money laundering detection |
| title_short | Comparative analysis of machine learning algorithms for money laundering detection |
| title_sort | comparative analysis of machine learning algorithms for money laundering detection |
| topic | Fraud detection Money laundering Machine learning Anomaly detection XGBoost |
| url | https://doi.org/10.1007/s44163-025-00397-4 |
| work_keys_str_mv | AT sundayadeolaajagbe comparativeanalysisofmachinelearningalgorithmsformoneylaunderingdetection AT simphiwemajola comparativeanalysisofmachinelearningalgorithmsformoneylaunderingdetection AT pragasenmudali comparativeanalysisofmachinelearningalgorithmsformoneylaunderingdetection |