[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118573-en":3,"doc-seo-118573-105":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},118573,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Leveraging Machine Learning to Minimize Fraudulent Transactions - A Strategic Modeling Approach","Fraud increasingly affects online financial ecosystems, prompting governments, organizations, and consumers to seek reliable ways to detect unusual and potentially harmful transactions. Machine learning supports fraud identification through data mining and automated pattern recognition, offering scalable and durable solutions. This work evaluates three machine learning approaches—GaussianNB, XGBoost, and logistic regression—using precision, recall, and F1 score as decision criteria. Results indicate that XGBoost provides the strongest fraud detection performance for WeGoWin.","A Work Project, presented as part ofthe requirements for the Award of a Master’s degree in Business Analytics from the Nova School of Business and Economics.  \nLEVERAGING MACHINE LEARNING TO MINIMIZE FRAUDULENT TRANSACTIONS: A STRATEGIC MODELING APPROACH  \nMAXIMILIAN RACH  \nWork project carried out under the supervision of:  \nQiwei Han  \nOdhiambo Dormnic  \n14/12/2024  \nAbstract  \nFraud has increasingly gained prevalence as millions of transactions are done online. Various stakeholders such as governments, organizations, and consumers have developed strategies to detect fraud and other unusual behavior. Machine learning techniques have been leveraged for fraud detection resulting in unique and sustainable solutions in financial transactions. In the modern age, machine learning algorithms have been widely utilized as a data mining technique for identifying issues with transactions. The current research aims to compare the effectiveness of three distinct machine learning models including GaussianNB, XGBoost, and Logistical Regression models by focusing on their precision, recall, and F1 score. Based on the outcomes of the three machine learning models, XGBoost is considered to be the best alternative for fraud detection at WeGoWin.  \nKeywords: Fraud detection, neural networks, machine learning, data mining  \nTable of Contents  \nIntroduction........................................................................................................................................... 5  \nResearch Significance ....................................................................................................................... 7  \nResearch Objectives .......................................................................................................................... 8  \nLiterature Review.................................................................................................................................. 8  \nFraud Detection................................................................................................................................. 9  \nTraditional Fraud Detection Strategies ........................................................................................... 11  \nMachine Learning: Algorithm and Neural Networks ..................................................................... 12  \nMachine Learning Algorithms utilized in Fraud Detection ............................................................ 13  \nMethods .............................................................................................................................................. 15  \nDesigning the Machine Learning Model ........................................................................................ 17  \nModel: Introduction .................................................................................................................... 17  \nOverview..................................................................................................................................... 17  \nExploratory Data Analysis (EDA) .............................................................................................. 18  \nPreprocessing .............................................................................................................................. 18  \nFeature Selection and Modeling ................................................................................................. 19  \nModel Validation ........................................................................................................................ 19  \nModel: Conclusion ...................................................................................................................... 19  \nResults................................................................................................................................................. 20  \nModel: Introduction .......................................................................","cbCaiq0A3UzIi7fx","https://ap.wps.com/l/cbCaiq0A3UzIi7fx","pdf",2696024,1,41,"English","en",105,"# Introduction\n## Research Significance\n## Research Objectives\n## Literature Review\n## Fraud Detection\n### Traditional Fraud Detection Strategies\n### Machine Learning: Algorithm and Neural Networks\n### Machine Learning Algorithms utilized in Fraud Detection\n# Methods\n## Designing the Machine Learning Model\n## Model: Introduction\n## Exploratory Data Analysis (EDA)\n## Preprocessing\n## Feature Selection and Modeling\n## Model Validation\n## Model: Conclusion\n# Results\n## Model: Introduction\n## User’s Data\n## Card’s Data\n## Transaction’s Data\n## Overview\n## EDA\n## Preprocessing\n## Feature Selection and Modeling\n## Model Validation\n## Balanced Accuracy Score\n## Classification Report\n## Confusion Matrix\n# Conclusion\n## Proposed Solution\n# References","[{\"question\":\"Which machine learning models are compared for fraud detection in this study?\",\"answer\":\"The research compares GaussianNB, XGBoost, and logistic regression to evaluate different modeling approaches for detecting fraudulent transactions.\"},{\"question\":\"What evaluation metrics are used to compare model performance?\",\"answer\":\"Model performance is assessed using precision, recall, and the F1 score.\"},{\"question\":\"Which model performs best for fraud detection at WeGoWin?\",\"answer\":\"Based on the reported outcomes, XGBoost is identified as the best alternative for fraud detection at WeGoWin.\"}]","Leveraging Machine Learning to Minimize Fraudulent Transactions - A Strategic Modeling Approach | PDF",1785684324,103,{"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},"leveraging-machine-learning-to-minimize-fraudulent-transactions-a-strategic-modeling-approach","",{"@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/leveraging-machine-learning-to-minimize-fraudulent-transactions-a-strategic-modeling-approach/118573/",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 models are compared for fraud detection in this study?","Question",{"text":75,"@type":76},"The research compares GaussianNB, XGBoost, and logistic regression to evaluate different modeling approaches for detecting fraudulent transactions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What evaluation metrics are used to compare model performance?",{"text":80,"@type":76},"Model performance is assessed using precision, recall, and the F1 score.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performs best for fraud detection at WeGoWin?",{"text":84,"@type":76},"Based on the reported outcomes, XGBoost is identified as the best alternative for fraud detection at WeGoWin.","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"]