[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119181-en":3,"doc-seo-119181-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},119181,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Bibliometric Analysis and Visualization of Machine Learning-Based Credit Card Fraud Detection","Machine learning is widely applied to credit card fraud detection by analyzing large transaction datasets and revealing patterns of suspicious behavior. This study aims to map the research landscape, identify annual topics, and provide references for future work in machine learning for credit card fraud detection. A three-phase methodology is used: descriptive statistics via Scopus web and Publish or Perish, citation analysis through a bibliometric approach, and visualization with VOSviewer. Results show three publication clusters and highlight a growing trend in classification under imbalanced and limited fraud data.","2024 International Conference on Information Technology Research and Innovation (ICITRI) | 979-8-3503-762 1-0/24/$31.00 ©2024 IEEE | DOI: 10. 1 109/ICITRI62858 .2024. 10699070  \nBibliometric Analysis and Visualization of Machine Learning-Based Credit Card Fraud Detection  \n1st Suhartono  \nDepartment of information engineering UIN Maulana Malik Ibrahim Malang Malang, Indonesia [suhartono@ti.uin-malang.ac.id](suhartono@ti.uin-malang.ac.id)  \n2nd Syahiduz Zaman  \nDepartment of information engineering UIN Maulana Malik Ibrahim Malang Malang, Indonesia [syahid@ti.uin-malang.ac.id](syahid@ti.uin-malang.ac.id)  \n3rd Totok Chamidy  \nDepartment of information engineering UIN Maulana Malik Ibrahim Malang Malang, Indonesia [to2k2013@ti.uin-malang.ac.id](to2k2013@ti.uin-malang.ac.id)  \nAbstract— Machine learning is often used in credit card fraud detection. Its ability to analyze large amounts of transaction data and identify patterns of fraudulent activity. The aim of this study is to provide insights into the research status, mapping process and annual topics of machine learning research on credit card fraud detection, and to provide relevant references for future research. Research phases with three approaches. The first approach is descriptive statistical analysis for data collection using Scopus web and Herzing’s Publish or Perish. The second approach uses quantitative methods for citation analysis with a bibliometric approach using Vos Viewer application. The research findings are related to the analysis of credit card fraud research divided into three clusters, the first cluster is the research stream using machine learning in data mining, the second cluster is the research stream using machine learning to detect credit card fraud, the third cluster is the research stream using machine learning to classify credit card fraud. The second cluster can resolve the complexity of credit card transaction data and increase the accuracy of the fraud detection system, for the third cluster, we can build a more effective classification in resolving imbalanced data and limited transaction records. A growing research trend is in the third cluster, research related to the performance of credit card fraud classification based on unbalanced data and limited fraud data. Further research is recommended to examine the evolution of the literature on the use of machine learning to detect credit card fraud and to conduct comparative studies between the Scopus, Web of Science and ScienceDirect databases to expand the literature.  \nKeywords—Machine learning; detection; fraud; credit cards; analysis; bibliometrics; visualization  \nI. INTRODUCTION  \nCurrently, research into credit card fraud detection is important because credit card usage is increasing every year, which is accompanied by an increase in fraudulent activity in credit card transactions. Implementing an efficient fraud detection system is mandatory for credit card issuing financial institutions to minimize credit card fraud, according to Raj & Portia [1] . Financial transactions are very important for economic growth. Therefore, prevention through credit card fraud detection serves to maintain public trust in credit card transactions [2] . Therefore, it is necessary to develop a fraud detection model for credit card transactions to minimize losses for credit card customers and maintain the integrity of credit card transactions [3] .  \nMachine learning is a branch of artificial intelligence that focuses on developing techniques that enable systems to learn from data and make predictions or decisions without external programming [4] . In the context of developing credit card fraud detection models, machine learning is often used to  \nanalyse transaction patterns and detect suspicious activities [5]. The use of machine learning to detect credit card fraud has made significant progress in recent years. Various studies have proposed various machine learning methods such as deep learning, supervised ","cbCaicCoHFX7UGfx","https://ap.wps.com/l/cbCaicCoHFX7UGfx","pdf",882085,1,6,"English","en",105,"# Introduction\n## Motivation for credit card fraud detection\n## Role of machine learning\n## Study objective and bibliometric approach\n## Research questions","[{\"question\":\"What is the main objective of this bibliometric study?\",\"answer\":\"To provide insights into the research status, mapping process, and annual topics of machine learning research on credit card fraud detection, along with relevant references for future studies.\"},{\"question\":\"Which tools and databases are used in the study?\",\"answer\":\"Descriptive data are collected using Scopus web and Publish or Perish, and citation/bibliometric analysis is performed with a bibliometric approach using VOSviewer for visualization.\"},{\"question\":\"What key findings are reported about research clusters?\",\"answer\":\"The study groups credit card fraud research into three clusters: using machine learning in data mining, using machine learning to detect fraud, and using machine learning to classify fraud—showing especially strong growth in classification under imbalanced and limited fraud data.\"}]","Bibliometric Analysis and Visualization of Machine Learning-Based Credit Card Fraud Detection | PDF",1785722955,15,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"bibliometric-analysis-and-visualization-of-machine-learning-based-credit-card-fraud-detection","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/bibliometric-analysis-and-visualization-of-machine-learning-based-credit-card-fraud-detection/119181/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main objective of this bibliometric study?","Question",{"text":76,"@type":77},"To provide insights into the research status, mapping process, and annual topics of machine learning research on credit card fraud detection, along with relevant references for future studies.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which tools and databases are used in the study?",{"text":81,"@type":77},"Descriptive data are collected using Scopus web and Publish or Perish, and citation/bibliometric analysis is performed with a bibliometric approach using VOSviewer for visualization.",{"name":83,"@type":74,"acceptedAnswer":84},"What key findings are reported about research clusters?",{"text":85,"@type":77},"The study groups credit card fraud research into three clusters: using machine learning in data mining, using machine learning to detect fraud, and using machine learning to classify fraud—showing especially strong growth in classification under imbalanced and limited fraud data.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]