[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118682-en":3,"doc-seo-118682-105":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},118682,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Machine Learning Methods for Credit Card Fraud Detection - Bachelor Thesis","Rising digital banking adoption accelerates cyber-security risks and intensifies the challenge of identifying and stopping fraud as online transactions grow. This bachelor thesis evaluates machine learning models for fraud detection using an anonymized European credit card dataset from Kaggle. Decision Tree, Logistic Regression, XGBoost, Random Forest, LightGBM, CatBoost, and Multi-Layer Perceptron are trained and tested with performance assessed via F1-score, precision, recall, and ROC-AUC, showing strong accuracy in separating legitimate from fraudulent transactions and supporting improved cyber resilience.","THESIS – BACHELOR'S DEGREE  \nTECHNOLOGY, COMMUNICATION AND TRANSPORT  \nMACHINE LEARNING METHODS FOR CREDIT CARD FRAUD DETECTION  \nAUTHOR/S MD IMRAN SAIYAM  \nSAVONIA UNIVERSITY OF APPLIED SCIENCES THESIS  \nAbstract  \n\n| Field of Study\u003Cbr>Technology, Communication and Transport |  |\n| --- | --- |\n| Degree Programme\u003Cbr>Degree Programme in Information Technology |  |\n| Author\u003Cbr>Md Imran Saiyam |  |\n| Title of Thesis\u003Cbr>Machine Learning Methods for Credit Card Fraud Detection |  |\n| Date 15.12.2025 | Pages/Appendices 31 |\n| Client Organisation /Partners\u003Cbr>Savonia University of Applied Science |  |\n| Abstract\u003Cbr>The rising popularity of digital banking has reshaped the financial environment and at the same time exposed the institutions to escalating cyber-security threats. With the growth of online transactions, so does the difficulty of identifying and stopping fraudulent acts and this is where the system on traditional fails to identify the dynamic and intricate patterns of fraud in real time which makes it need smarter and adaptable solutions. In this study , the capabilities of machine learning models were explored to enhance cyber security on an anonymized European credit card fraud dataset on Kaggle, a collection of machine learning algorithms as well as Decision Tree, Logistic Regression, XGBoost, Random Forest , LightGBM, CatBoost, and Multi-Layer Perceptron were implemented and tested. The most useful model used in detecting fraud was identified based on key performance indicators like F1-score , precision, recall, and ROC-AUC. Overall, the research indicated that properly trained ML models may distinguish between legitimate and fraud transactions with a high level of accuracy. Overall, the study shows the significance of machine learning as an efficient instrument of improving cyber resilience in the age of digital finance. |  |\n| Keywords\u003Cbr>Credit Card Fraud Detection, Machine Learning, Feature Selection , Logistic Regression , Decision Tree, Random Forest, XGBoost, LightGBM , CatBoost. |  |\n\nCONTENTS  \n1 INTRODUCTION ....................................................................................................................... 5  \n1.1 Overview............................................................................................................................................. 5  \n1.2 Problem Description ........................................................................................................................... 6  \n1.3 Study Objectives ................................................................................................................................ 6  \n1.4 Research Contributions ...................................................................................................................... 7  \n2 LITERATURE REVIEW ............................................................................................................. 8  \n2.1 Overview............................................................................................................................................. 8  \n2.2 Machine learning for detecting fraud in digital banking ...................................................................... 8  \n2.3 Challenges in Fraud Detection Research .......................................................................................... 8  \n2.4 Research Gap and Contribution ......................................................................................................... 9  \n3 RESEARCH METHODOLOGY................................................................................................ 10  \n3.1 Data source and collection ............................................................................................................... 10  \n3.2 Data Preprocessing .......................................................................................................................... 12  \n3.3 Feature Scaling ................................................","cbCaimqHMZ2ZtgEV","https://ap.wps.com/l/cbCaimqHMZ2ZtgEV","pdf",558943,1,31,"English","en",105,"# 1 INTRODUCTION\n## 1.1 Overview\n## 1.2 Problem Description\n## 1.3 Study Objectives\n## 1.4 Research Contributions\n# 2 LITERATURE REVIEW\n## 2.1 Overview\n## 2.2 Machine learning for detecting fraud in digital banking\n## 2.3 Challenges in Fraud Detection Research\n## 2.4 Research Gap and Contribution\n# 3 RESEARCH METHODOLOGY\n## 3.1 Data source and collection\n## 3.2 Data Preprocessing\n## 3.3 Feature Scaling\n## 3.4 Train-Test Split\n## 3.5 Handling Class Imbalance\n## 3.6 Machine Learning Algorithms\n## 3.6.1 Decision Tree (DT)\n## 3.6.2 Random Forest (RF)\n## 3.6.3 Extreme Gradient Boosting (XGBoost)\n## 3.6.4 CatBoost\n## 3.6.5 Light Gradient Boosting Machine (LightGBM)\n## 3.6.6 Neural Network (NN)\n## 3.6.7 Logistic Regression (LR)\n# 4 EXPERIMENTAL RESEARCH AND ANALYSIS\n## 4.1 Experimental Setup\n## 4.2 Classification Measures\n## 4.3 Confusion Matrix Analysis for Different Models","[{\"question\":\"Which machine learning algorithms are implemented for credit card fraud detection?\",\"answer\":\"The study implements Decision Tree, Logistic Regression, XGBoost, Random Forest, LightGBM, CatBoost, and Multi-Layer Perceptron (neural network).\"},{\"question\":\"How is model performance evaluated in the thesis?\",\"answer\":\"Models are assessed using F1-score, precision, recall, and ROC-AUC as key performance indicators. The best fraud-detecting model is selected based on these metrics.\"},{\"question\":\"What dataset and preprocessing steps are used?\",\"answer\":\"The research uses an anonymized European credit card fraud dataset from Kaggle, followed by data preprocessing, feature scaling, and a train-test split. It also addresses class imbalance during training.\"}]","Machine Learning Methods for Credit Card Fraud Detection - Bachelor Thesis | PDF",1785684866,78,{"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},"machine-learning-methods-for-credit-card-fraud-detection-bachelor-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/machine-learning-methods-for-credit-card-fraud-detection-bachelor-thesis/118682/",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},"Which machine learning algorithms are implemented for credit card fraud detection?","Question",{"text":75,"@type":76},"The study implements Decision Tree, Logistic Regression, XGBoost, Random Forest, LightGBM, CatBoost, and Multi-Layer Perceptron (neural network).","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is model performance evaluated in the thesis?",{"text":80,"@type":76},"Models are assessed using F1-score, precision, recall, and ROC-AUC as key performance indicators. The best fraud-detecting model is selected based on these metrics.",{"name":82,"@type":73,"acceptedAnswer":83},"What dataset and preprocessing steps are used?",{"text":84,"@type":76},"The research uses an anonymized European credit card fraud dataset from Kaggle, followed by data preprocessing, feature scaling, and a train-test split. 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