[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121733-en":3,"doc-seo-121733-105":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":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},121733,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Credit Card Fraud Detection Using Machine Learning Algorithms","Credit card fraud poses a major threat to online business security, driving the need for fast and accurate detection methods. The work presents a machine learning–based approach using a multilayer perceptron (MLP) and a decision tree, trained and evaluated on a public card-fraud dataset with 1,000,000 transactions and significant class imbalance. To address imbalance, random undersampling, SMOTE, and SMOTE-Tomek are applied. Results show the best performance when MLP and the decision tree are trained with SMOTE-Tomek, while dimensionality reduction of input features causes a smaller performance drop for MLP, indicating robustness.","138 STRUČNI RAD UDK 004.8  \nCredit Card Fraud Detection Using Machine Learning Algorithms  \nIvan Lorencin1, Nikola Anđelić2, Deni Vale3, Marko Mavrinac4  \n[1](1 dr. sc)[ dr. sc](1 dr. sc).; Istarsko veleučilište, Riva 6, Pula, Hrvatska  \n[2](2 dr. sc. Sveu)[ dr. sc. Sveu](2 dr. sc. Sveu)čilište u Rijeci – Tehnički fakultet, Vukovarska , Rijeka, Hrvatska  \n3 mag. phys.; Istarsko veleučilište, Riva 6, Pula, Hrvatska  \n[4](4 mag. ing. el. Elektroindustrijska)[ mag. ing. el. Elektroindustrijska](4 mag. ing. el. Elektroindustrijska) i obrtnička škola Rijeka, Zvonimirova 12, Rijeka, Hrvatska  \nAbstract  \nOne of the main challenges to the security of an online business is credit card fraud. For this reason, algorithms based on artificial intelligence and machine learning are being introduced to enable the most accurate and fast detection of card fraud. This paper presents an approach to the detection of card fraud based on machine learning algorithms more specifically, a multilayer perceptron (MLP) and a Decision tree. The aforementioned algorithms were trained and tested using a publicly available data set on card fraud. The data set used consists of 7 characteristics of the card transaction and information on whether there was card fraud or not. In total, the data set contains information on 1,000,000 transactions, and it is highly imbalanced. To handle the class imbalance, random undersampling, SMOTE, and SMOTE-Tomek algorithms were proposed. From the achieved results it can be seen that the highest performances are achieved if MLP (AUC = 0.99, f1 = 0.99, MCC = 0.98, and Kappa = 0.98) and Decision tree (AUC = 0.99, f1 = 0.99, MCC = 0.99, and Kappa = 0.98) are trained by using data set re-sampled by using SMOTE-Tomek algorithm. If the performance of the mentioned algorithms is examined using fewer characteristics of the transaction, it can be seen that by reducing the number of characteristics a significant decrease in classification performances can be noticed ifa Decision tree in combination with SMOTE-Tomek is used. However, if an MLP in combination with SMOTE-Tomek is used, a significantly lower decrease in performance can be observed, pointing to the higher robustness to input vector dimension reduction. Such a robust system can provide information about transaction validity even in a condition where the input data is limited to a few input variables. From the achieved results, it can be concluded that MLP in combination with the SMOTE-Tomek algorithm can be used for credit card fraud detection, even in conditions with a lower number of input variables.  \nZbornik Istarskog veleučilišta Vol. 2 (2023.), No. 1  \n139  \nCredit Card Fraud Detection Using Machine Learning Algorithms  \n1. Introduction  \nOnline trading has taken a large share in worldwide trade in recent years (Roufet al., 2021) . More and more different products and services can be bought and sold online (Melović et al., 2021) . Such a trend reached its peak in the past years due to the COVID-19 pandemic and reduced physical contact (Wynn and Olayinka, 2021) . For the above reasons, safe and verified trade is imperative for the exchange of goods and services to be carried out smoothly and without major delays (Kim et al., 2022) . One of the main challenges for the security of an online business is certainly card fraud, and the timely detection of fraud represents a significant saving of resources and time (Arora et al., 2022) . For this reason, algorithms based on artificial intelligence (AI) and machine learning (ML) are being introduced to enable the most accurate and fast detection of card fraud (Alarfaj et al., 2022) . Utilization of AI and ML has an application in the security area, ranging from protection of (Internet of Things) IoT systems and computer networks (Kuzlu et al., 2021, Mattos et al., 2020, Qiu et al., 2019), over financial systems (Melnychenko, 2020, Bredt, 2019) to video surveillance (Nguyen et al., 2020, Lorencin et al., 2019) . Similar technique","cbCaikRVgDtjy1YD","https://ap.wps.com/l/cbCaikRVgDtjy1YD","pdf",1081174,1,25,"English","en",105,"# Abstract\n## Introduction","[{\"question\":\"What machine learning methods are used for credit card fraud detection?\",\"answer\":\"The paper uses a multilayer perceptron (MLP) and a decision tree to classify transactions as fraudulent or not.\"},{\"question\":\"How does the study handle the class imbalance in the dataset?\",\"answer\":\"It applies random undersampling, SMOTE, and SMOTE-Tomek to rebalance the training data.\"},{\"question\":\"Which resampling approach yields the highest reported performance?\",\"answer\":\"The highest performance is reported when both MLP and the decision tree are trained on data resampled with SMOTE-Tomek.\"}]","Credit Card Fraud Detection Using Machine Learning Algorithms | PDF",1785806545,63,{"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","",{"@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/121733/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What machine learning methods are used for credit card fraud detection?","Question",{"text":75,"@type":76},"The paper uses a multilayer perceptron (MLP) and a decision tree to classify transactions as fraudulent or not.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study handle the class imbalance in the dataset?",{"text":80,"@type":76},"It applies random undersampling, SMOTE, and SMOTE-Tomek to rebalance the training data.",{"name":82,"@type":73,"acceptedAnswer":83},"Which resampling approach yields the highest reported performance?",{"text":84,"@type":76},"The highest performance is reported when both MLP and the decision tree are trained on data resampled with SMOTE-Tomek.","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"]