[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118414-en":3,"doc-seo-118414-105":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},118414,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Credit Card Fraud Detection Using Machine Learning Algorithms - Master Thesis","Existing credit card fraud detection research often relies on imbalanced datasets, using sampling strategies before applying machine learning models. This thesis evaluates the reliability of machine learning methods on a balanced credit card dataset and investigates their effectiveness when confronting imbalance. Using Kaggle’s balanced dataset with 568,630 transactions, the study compares Random Forest, Neural Networks, Logistic Regression, and Naive Bayes, finding Random Forest and Neural Networks reach near-perfect accuracy (99.9%), supporting improved fraud detection systems for financial institutions.","UCLA  \nUCLA Electronic Theses and Dissertations  \nTitle  \nCredit Card Fraud Detection Using Machine Learning Algorithms  \nPermalink  \n[https://escholarship.org/uc/item/8qm795fk](https://escholarship.org/uc/item/8qm795fk)  \nAuthor  \nFeng, Xingyu  \nPublication Date  \n2025  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA  \nLos Angeles  \nCredit Card Fraud Detection Using Machine Learning Algorithms  \nA thesis submitted in partial satisfaction of the requirements for the degree Master of Applied Statistics and Data Science  \nby  \nXingyu Feng  \n© Copyright by Xingyu Feng 2025  \nABSTRACT OF THE THESIS  \nCredit Card Fraud Detection Using Machine Learning Algorithms  \nby  \nXingyu Feng  \nMaster of Applied Statistics and Data Science  \nUniversity of California, Los Angeles, 2025  \nProfessor Yingnian Wu, Chair  \nExisting credit card fraud detection studies are generally based on imbalanced datasets, where various sampling methods are used to alleviate the imbalance before applying machine learning techniques for detection. This study applies machine learning methods to a balanced credit card dataset to evaluate their reliability on imbalanced datasets. Using Kaggle’s balanced dataset of 568,630 transactions, we compared Random Forest, Neural Networks, Logistic Regression and Naive Bayes. Random Forest and Neural Networks achieved near-perfect accuracy (99.9%), demonstrating superior fraud detection capabilities that could significantly enhance financial institutions’ security systems. These findings highlight the significant potential of these advanced machine learning methods to optimize fraud detection systems in financial institutions and provide strong support to further improve fraud detection capabilities.  \nThe thesis of Xingyu Feng is approved.  \nRobert L Gould David Anthony Zes Hongquan Xu Yingnian Wu, Committee Chair  \nUniversity of California, Los Angeles 2025  \nTABLE OF CONTENTS  \n1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1  \n2 Exploratory Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4  \n2.1 Data Information ................................... 4  \n2.2 Variable Introduction ................................. 4  \n2.3 Research Questions .................................. 4  \n2.4 Exploratory Data Analysis .............................. 5  \n3 Logistic Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8  \n3.1 Logistic Regression Introduction ........................... 8  \n3.2 Model Analysis .................................... 9  \n4 Random Forest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18  \n4.1 Random Forest Introduction ............................. 18  \n4.2 Model Analysis .................................... 20  \n5 Backpropagation Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . 24  \n5.1 Backpropagation Neural Network Introduction ................... 24  \n5.2 Model Analysis .................................... 25  \n6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28  \nLIST OF FIGURES  \n2.1 Check if the categories are balanced.......................... 6  \n2.2 The Pearson correlation coefficient between independent variables ......... 7  \n3.1 Logistic regression results for Model1 ........................ 10  \n3.2 Confusion matrix and accuracy of Model1 on test set ................ 11  \n3.3 VIF values between independent variables in Model 1 ................ 12  \n3.4 Logistic regression results for Model2 ........................ 14  \n3.5 Confusion matrix and accuracy of Model2 on test set ................ 15  \n3.6 Logistic regression results for Model3 ......................... 16  \n3.7 Confusion matrix and accuracy of Model3 on test set ................ 17  \n4.1 Random Forest result ......................","cbCaidj3KpRpwvps","https://ap.wps.com/l/cbCaidj3KpRpwvps","pdf",2145426,1,35,"English","en",105,"# 1 Introduction\n# 2 Exploratory Data Analysis\n## 2.1 Data Information\n## 2.2 Variable Introduction\n## 2.3 Research Questions\n## 2.4 Exploratory Data Analysis\n# 3 Logistic Regression\n## 3.1 Logistic Regression Introduction\n## 3.2 Model Analysis\n# 4 Random Forest\n## 4.1 Random Forest Introduction\n## 4.2 Model Analysis\n# 5 Backpropagation Neural Network\n## 5.1 Backpropagation Neural Network Introduction\n## 5.2 Model Analysis\n# 6 Conclusion","[{\"question\":\"Why does this thesis focus on balanced datasets for fraud detection?\",\"answer\":\"Because many prior studies use imbalanced datasets and apply sampling before modeling. This thesis applies machine learning on a balanced dataset to test reliability when confronting imbalance.\"},{\"question\":\"Which machine learning models are compared in the study?\",\"answer\":\"Random Forest, Neural Networks, Logistic Regression, and Naive Bayes are compared using Kaggle’s balanced dataset of 568,630 transactions.\"},{\"question\":\"What results does the thesis report for Random Forest and Neural Networks?\",\"answer\":\"Random Forest and Neural Networks achieve near-perfect accuracy, reported as 99.9%, indicating strong fraud detection capability.\"}]","Credit Card Fraud Detection Using Machine Learning Algorithms - Master Thesis | PDF",1785683506,88,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"credit-card-fraud-detection-using-machine-learning-algorithms-master-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@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/credit-card-fraud-detection-using-machine-learning-algorithms-master-thesis/118414/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why does this thesis focus on balanced datasets for fraud detection?","Question",{"text":75,"@type":76},"Because many prior studies use imbalanced datasets and apply sampling before modeling. This thesis applies machine learning on a balanced dataset to test reliability when confronting imbalance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are compared in the study?",{"text":80,"@type":76},"Random Forest, Neural Networks, Logistic Regression, and Naive Bayes are compared using Kaggle’s balanced dataset of 568,630 transactions.",{"name":82,"@type":73,"acceptedAnswer":83},"What results does the thesis report for Random Forest and Neural Networks?",{"text":84,"@type":76},"Random Forest and Neural Networks achieve near-perfect accuracy, reported as 99.9%, indicating strong fraud detection capability.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"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":106,"slug":138},19,"General","general"]