[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128010-en":3,"doc-seo-128010-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128010,962084928904,"Asher","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning Techniques for Credit Card Fraud Detection - Thesis","Credit card fraudulent transactions are increasing across the financial market, generating significant losses for companies, organizations, and government agencies. Rapid and reliable early detection is therefore a priority, yet credit card fraud is difficult because fraudulent behavior varies between attempts and the dataset is highly imbalanced, with far more legitimate than fraudulent transactions. The thesis applies random oversampling and evaluates precision, recall, F-beta, and thresholds. Multiple predictive models are tested using resampling strategies and metrics such as recall, precision, F1-score, ROC AUC, and accuracy. Results show ANN performs best overall, and introducing LSTM variants improves accuracy up to 99.97%.","Montclair State University  \nMontclair State University Digital Commons  \nTheses, Dissertations and Culminating Projects  \n1-2023  \nMachine Learning Techniques for Credit Card Fraud Detection  \nAmal Alamri  \nMontclair State University  \nFollow this and additional works at: [https://digitalcommons.montclair.edu/etd](https://digitalcommons.montclair.edu/etd)  \n Part of the Artificial Intelligence and Robotics Commons, and the Finance and Financial Management Commons  \nRecommended Citation  \nAlamri, Amal, \"Machine Learning Techniques for Credit Card Fraud Detection\" (2023) . Theses, Dissertations and Culminating Projects. 1456.  \n[https://digitalcommons.montclair.edu/etd/1456](https://digitalcommons.montclair.edu/etd/1456)  \nThis Thesis is brought to you for free and open access by Montclair State University Digital Commons. It has been accepted for inclusion in Theses, Dissertations and Culminating Projects by an authorized administrator of Montclair State University Digital Commons. For more information, please contact [digitalcommons@montclair.edu](digitalcommons@montclair.edu).  \nMachine Learning Techniques for Credit Card Fraud Detection  \nA THESIS  \nSubmitted to the Faculty of Montclair State University  \nin partial fulfilment of the requirements  \nfor the degree of Master of Science  \nby  \nAmal Alamri  \nMontclair State University  \nMontclair, NJ  \n2023  \nMACHINE LEARNING TECHNIQUES FOR CREDIT CARD FRAUD DETECTION iii  \nCopyright@2023 by Amal Alamri. All rights reserved.  \nMACHINE LEARNING TECHNIQUES FOR CREDIT CARD FRAUD DETECTION iv  \nAbstract  \nCredit card fraudulent transactions are becoming an ever-growing problem in the financial market. There has been a rapid increase in the rate of fraudulent transactional activities in recent years producing considerable financial loss to many companies, organizations, and government agencies. These numbers are anticipated to increase in the near future and many scholars in this field are focused on detecting fraudulent transactions early on, using advanced Machine Learning techniques. However, credit card fraud transaction detection is not easy for two reasons: (I) fraudulent methods usually vary for each attempt, and (II) the dataset is extremely imbalanced with many more normal transactions than fraudulent cases. A random oversampling approach is used to solve this issue by utilizing precision, recall, F-beta scores, and thresholds. The original dataset contains 284,807 transactions with only 492 transactions labeled as fraudulent. Predictive models, such as logistic regression, random forest, decision tree, and Recurrent ANN with dense layers and sequential model, Adam optimizer, and cross-entropy are used, in combination with different resampling methods. The model performance is evaluated using metrics, including recall, precision, F1-score, ROC AUC score, and accuracy for fraud cases. Our experimental results indicate that ANN outperformed other models. Our ANN utilizes dense layers and some sequential models with optimizer and loss type. The ANN approach, combined with different resampling methods, has been implemented to predict the nature of a certain credit card transaction. To further enhance the performance, the Long Short Term Memory model (LSTM) with multilayer perception is introduced. Multilayer Recurrent Neural Network based LSTM performs better than artificial neural network-based models and improves overall model accuracy. It also improves the accuracy of fraudulent case prediction. We further explore RNN-LSTM and conduct experiments by introducing bi-directional LSTM. This Bi-  \nMACHINE LEARNING TECHNIQUES FOR CREDIT CARD FRAUD DETECTION v  \ndirectional LSTM improves the accuracy of the model to 99.97% . We also find out that some data fields of the credit card transaction have a higher contribution to the detection.  \nKeywords: Credit card fraudulent transactions, Fraud detection techniques, Data balancing, Machine learning, Artificial Neutral Network, LSTM an","cbCaibDfmmnYGSXB","https://ap.wps.com/l/cbCaibDfmmnYGSXB","pdf",3207131,4,1,43,"English","en",105,"# Chapter 1: Introduction\n## Types of Credit Card Fraud\n## Lost/Stolen cards\n## Synthetic Fraud\n## Merchant Abuse\n## Data Breach\n# Chapter 2: Literature Review\n# Chapter 3: Methodology\n## Dataset\n## Algorithm Design\n## Overview of a Few Machine Learning Algorithms\n## Logistic Regression\n## Decision Tree Algorithm\n## Random Forest Algorithm","[{\"question\":\"What makes credit card fraud detection particularly challenging in this thesis?\",\"answer\":\"Fraud patterns vary across attempts and the dataset is extremely imbalanced, with far more normal transactions than fraudulent ones.\"},{\"question\":\"How does the thesis address the class imbalance problem?\",\"answer\":\"It uses a random oversampling approach combined with evaluation using precision, recall, F-beta scores, and thresholds.\"},{\"question\":\"Which model achieved the best reported performance and how was it improved?\",\"answer\":\"An ANN outperformed other tested models, and adding LSTM-based recurrent architectures (including bi-directional LSTM experiments) further improved accuracy to 99.97%.\"}]","Machine Learning Techniques for Credit Card Fraud Detection - Thesis | PDF",1785943820,108,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"machine-learning-techniques-for-credit-card-fraud-detection-thesis","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/machine-learning-techniques-for-credit-card-fraud-detection-thesis/128010/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-25","2026-08-05",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 makes credit card fraud detection particularly challenging in this thesis?","Question",{"text":76,"@type":77},"Fraud patterns vary across attempts and the dataset is extremely imbalanced, with far more normal transactions than fraudulent ones.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the thesis address the class imbalance problem?",{"text":81,"@type":77},"It uses a random oversampling approach combined with evaluation using precision, recall, F-beta scores, and thresholds.",{"name":83,"@type":74,"acceptedAnswer":84},"Which model achieved the best reported performance and how was it improved?",{"text":85,"@type":77},"An ANN outperformed other tested models, and adding LSTM-based recurrent architectures (including bi-directional LSTM experiments) further improved accuracy to 99.97%.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]