[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123192-en":3,"doc-seo-123192-105":30,"detail-sidebar-cat-0-en-105":90},{"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},123192,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Anomalous Transaction Detection in Bank Credit Card Data Using Machine Learning - Research Paper","Illegal money changers create financial-system risks such as money laundering, fraud, and practices that undermine regulatory efforts for financial integrity. This paper investigates how machine learning models can improve the efficiency and accuracy of anti-money laundering measures by detecting suspicious patterns in bank card transactions. It develops a machine learning framework using large-scale transaction pre-processing, compares supervised and unsupervised methods (Random Forest, SVM, neural networks), and evaluates performance with precision/recall, F1-score, and ROC-AUC. It also examines interpretability to support regulatory transparency and analyst decision-making.","Kennesaw State University  \nDigitalCommons@Kennesaw State University  \n\n| African Conference on Information Systems and Technology | The 10th Annual ACIST Proceedings (2024) |\n| --- | --- |\n| Sep 12th, 12:15 PM-12:50 PM\u003Cbr>Anomalous Transaction Detection in Bank Credit Card Data Using Machine Learning\u003Cbr>Lerdinia Varaidzo Mapepa\u003Cbr>Women's University in Africa, [vmapepa@wua.ac.zw](vmapepa@wua.ac.zw)\u003Cbr>Jerremiah Musariwa\u003Cbr>Women's University in Africa, [jmusariwa@wua.ac.zw](jmusariwa@wua.ac.zw)\u003Cbr>Lucia Makwasha\u003Cbr>Women's University in Africa, [lmakwasha@wua.ac.zw](lmakwasha@wua.ac.zw)\u003Cbr>Samuel Mugijima\u003Cbr>Women's University in Africa, [smugijima@wua.ac.zw](smugijima@wua.ac.zw)\u003Cbr>Follow this and additional works at: [https://digitalcommons.kennesaw.edu/acist](https://digitalcommons.kennesaw.edu/acist)\u003Cbr> Part of the Other Computer Engineering Commons |  |\n\nMapepa, Lerdinia Varaidzo; Musariwa, Jerremiah; Makwasha, Lucia; and Mugijima, Samuel, \"Anomalous Transaction Detection in Bank Credit Card Data Using Machine Learning\" (2024) . African Conference on Information Systems and Technology. 9.  \n[https://digitalcommons.kennesaw.edu/acist/2024/presentations/9](https://digitalcommons.kennesaw.edu/acist/2024/presentations/9)  \nThis Event is brought to you for free and open access by the Conferences, Workshops, and Lectures at DigitalCommons@Kennesaw State University. It has been accepted for inclusion in African Conference on Information Systems and Technology by an authorized administrator of DigitalCommons@Kennesaw State University. For more information, please [contact digitalcommons@kennesaw.edu](contact digitalcommons@kennesaw.edu).  \nAnomalous Transaction Detection in Bank Credit Card Data Using Machine Learning  \nResearch Paper  \nLerdinia V. Mapepa  \nWomen’s University in Africa  \n[vmapepa@wua.ac.zw](vmapepa@wua.ac.zw)  \nLucia Makwasha  \nWomen’s University in Africa  \n[lmakwasha@wua.ac.zw](lmakwasha@wua.ac.zw)  \nJerremiah Musariwa  \nWomen’s University in Africa  \n[j](jmusariwa@wua.ac.zw)[musariwa@wua.ac.zw](jmusariwa@wua.ac.zw)  \nSamuel Mugijima  \nWomen’s University in Africa  \n[smugijima@wua.ac.zw](smugijima@wua.ac.zw)  \nABSTRACT  \nIllegal money changers pose a number of risks to the financial system, including but not limited to money laundering, fraud, and other under-the-carpet dealings intended to frustrate regulatory efforts for financial integrity. The efficiency and accuracy of anti-money laundering (AML) measures using machine learning (ML) models in the detection of suspicious patterns in bank card transactions are investigated in this paper. The key focus will be to develop an efficient machine learning framework that should be proficient in underlining main transactions dealing with illegal money changers and other similar fraudulent activities. The features indicative of illicit behaviour are determined by a comprehensive approach with the pre-processing of largescale datasets of transactions. Compared are performances for various ML algorithms, including supervised and unsupervised techniques, using Random Forest, Support Vector Machines, and neural networks for effective anomaly detection. Performance metrics included precision recall, F1-score, and area under the Receiver Operating Characteristic (ROC) curve, that will dictate the capability of models in discerning legitimate from suspicious transactions. Besides, the present work further investigates the aspect of interpretability of ML models, focusing on why transparency of the AML process has to be attained by data mining methods in order to meet regulatory standards and support human analysts' decisions.  \nKeywords  \nMachine Learning, Anomaly Detection, Money Laundering, Financial Fraud Detection, Illegal Money Changers, Anti-Money Laundering.","cbCaieuCnGZP8J4Z","https://ap.wps.com/l/cbCaieuCnGZP8J4Z","pdf",123540,1,2,"English","en",105,"# Abstract\n# Keywords\n# Method Focus\n## Model Comparison\n## Evaluation Metrics\n## Interpretability and AML Transparency","[{\"question\":\"What problem does the paper address in bank card transactions?\",\"answer\":\"The paper addresses risks posed by illegal money changers, including money laundering and fraud, by targeting suspicious patterns in bank credit card transaction data.\"},{\"question\":\"Which machine learning methods are compared for anomaly detection?\",\"answer\":\"The study compares multiple machine learning algorithms, including Random Forest, Support Vector Machines, and neural networks, using both supervised and unsupervised techniques.\"},{\"question\":\"How is model performance evaluated in the paper?\",\"answer\":\"Performance is evaluated using precision, recall, F1-score, and area under the ROC curve to measure how well models distinguish legitimate from suspicious transactions.\"}]","Anomalous Transaction Detection in Bank Credit Card Data Using Machine Learning - Research Paper | PDF",1785815129,5,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":28},"anomalous-transaction-detection-in-bank-credit-card-data-using-machine-learning-research-paper","",{"@graph":36,"@context":84},[37,53,67],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":21},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/anomalous-transaction-detection-in-bank-credit-card-data-using-machine-learning-research-paper/123192/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does the paper address in bank card transactions?","Question",{"text":74,"@type":75},"The paper addresses risks posed by illegal money changers, including money laundering and fraud, by targeting suspicious patterns in bank credit card transaction data.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which machine learning methods are compared for anomaly detection?",{"text":79,"@type":75},"The study compares multiple machine learning algorithms, including Random Forest, Support Vector Machines, and neural networks, using both supervised and unsupervised techniques.",{"name":81,"@type":72,"acceptedAnswer":82},"How is model performance evaluated in the paper?",{"text":83,"@type":75},"Performance is evaluated using precision, recall, F1-score, and area under the ROC curve to measure how well models distinguish legitimate from suspicious transactions.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,108,113,118,121,126,129,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":105,"show_sort_weight":106,"slug":107},"Comic",60,"comic",{"id":109,"doc_module":4,"doc_module_name":46,"category_name":110,"show_sort_weight":111,"slug":112},6,"Technology",50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":119,"slug":120},30,"research-report",{"id":122,"doc_module":4,"doc_module_name":46,"category_name":123,"show_sort_weight":124,"slug":125},9,"Religion & Spirituality",20,"religion-spirituality",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":124,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":29,"slug":136},19,"General","general"]