[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125433-en":3,"doc-seo-125433-105":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},125433,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","A Review of Credit Card Fraud Detection Using Machine Learning","Credit card fraud is increasing with the growth of online payments on e-commerce platforms, causing significant financial losses to banks, merchants, and organizations. Effective identification requires secure methods because fraudsters attempt to disguise fraudulent transactions as legitimate. The paper discusses hybrid algorithms and artificial neural networks for detection, using dataset variables such as duration, transaction amount, and V1–V28 as derived parameters to separate fraudulent transactions from non-fraudulent ones.","A Review of Credit Card Fraud Detection Using Machine Learning  \nN.S. Shroff1*, A.S. Vaishnav2  \n1Department of Computer Engineering, Government Engineering College, Gandhinagar, India 2Department of Computer Engineering, Government Polytechnic, Gandhinagar, India  \n*[Corresponding Author: amitvaishnav1112@gmail.com](Corresponding Author: amitvaishnav1112@gmail.com)  \nReceived: 21/Feb/2023, Accepted: 20/Mar/2023, Published: 30/Apr/2023  \nAbstract-Nowadays fraud has been increasing due to the establishment of online payment mode on different E-commerce platform.A credit card is a form of payment that lets you buy goods or services on credit from an issuer, usually a bank. You can make purchases up to a specified limit and then pay them off over time either in full or with minimum payments.There are several types of security features including fraud protection, verified by visa and master card secure code, address verification systems, and biometric authentication. Additionally, some cards offer the additional security feature of a chip and pin system which requires that the cardholder enter a secret code to make purchases.Still fraud has been executed using this card. In this fraud, banks, merchants, and organisations are losing billions of dollars. According to one survey, the prevalence of credit card fraud is rising by 12.5% a year. It is crucial to identify fraud using secure and effective methods.  \nNowadays, hybrid algorithms and artificial neural networks are used to detect fraud since they perform better than other methods. We will use dataset variables like \"duration,\" \"amount of transaction,\" and \"V1 to V28\" as derived parameters for this. We will build a model that will separate out fraudulent transactions from other transactions using machine learning techniques or algorithms.  \nKeywords—Machine learning, Hybrid algorithms, Fraud, Fraudulent and Credit card  \nI. INTRODUCTION  \nFraud is defined as stealing something that belongs to someone else without the user's knowledge. There are several types of fraud, including online fraud, offline fraud, and resource fraud. Among all this credit fraud that is categorised under \"online frauds,\" it is nowadays becoming a major problem. Credit card fraud detection is avery tough and difficult process to detect since fraudsters always attempt to pass off every fraudulent transaction as legitimate.  \nCredit card fraud can be done by using the information of any person to perform different transactions.Thereare different ways to commit credit card fraud by stealing the customer's information.  \n􀂾Directly from the customer  \n􀂾Through a payment gateway, such as PayPal or Stripe.  \n􀂾Through a third-party credit card processor, such as Square or Adyen.  \n􀂾Through a merchant account provider, such as First Data or Worldpay.  \n􀂾Through a mobile wallet, such as Apple Pay or Google Pay.  \n􀂾Through a credit card reader, such as a Point of Sale (POS) system.  \nWe can control this type of fraud by making people aware of it and reducing the financial loss that different organisations have to bear.  \nGlobal losses due to credit card fraud were around 1,68,260 crores of Indian rupees in 2017 and are expected to steadily rise by 2020, when they are projected to reach 2,28,775 crores of Indian rupees. Over 2.9 crore people in India currently use credit cards.But since the development of technology, Cybercrime is done from different places in the world, and in India, Jamtara has been the hub of cybercrime for the past five years. In 2019, 107 Jamtara citizens were detained on suspicion of cybercrime[4] . According to Reserve Bank of India (RBI) data, fraudsters stole 615.39 crore in more than 1.17 lakh cases of credit and debit card theft over a ten-year period (April 2009 to September 2019) [15] .  \nThere are many challenges faced while designing and implementing credit fraud detection techniques, such asthe unavailability of datasets due to security reasons, imbalanced data, operational efficienc","cbCaihilAuKXy0vK","https://ap.wps.com/l/cbCaihilAuKXy0vK","pdf",576833,1,6,"English","en",105,"# Introduction\n## Types and channels of credit card fraud\n## Challenges in fraud detection\n## Algorithms and evaluation metrics\n# Related Work","[{\"question\":\"Why is credit card fraud detection difficult?\",\"answer\":\"Fraudsters try to make fraudulent transactions look legitimate, making classification challenging. The task also faces issues like dataset unavailability and imbalanced data.\"},{\"question\":\"What approach does the study focus on for fraud detection?\",\"answer\":\"The study focuses on hybrid algorithms and artificial neural networks to improve detection performance. It uses derived dataset variables such as duration, transaction amount, and V1 to V28.\"},{\"question\":\"Which performance indicators are used to compare detection algorithms?\",\"answer\":\"Accuracy, specificity, and precision are used to evaluate and compare how well algorithms distinguish legal from illegal transactions.\"}]","A Review of Credit Card Fraud Detection Using Machine Learning | PDF",1785898885,15,{"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},"a-review-of-credit-card-fraud-detection-using-machine-learning","",{"@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/a-review-of-credit-card-fraud-detection-using-machine-learning/125433/",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","2026-08-05",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 is credit card fraud detection difficult?","Question",{"text":75,"@type":76},"Fraudsters try to make fraudulent transactions look legitimate, making classification challenging. The task also faces issues like dataset unavailability and imbalanced data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What approach does the study focus on for fraud detection?",{"text":80,"@type":76},"The study focuses on hybrid algorithms and artificial neural networks to improve detection performance. It uses derived dataset variables such as duration, transaction amount, and V1 to V28.",{"name":82,"@type":73,"acceptedAnswer":83},"Which performance indicators are used to compare detection algorithms?",{"text":84,"@type":76},"Accuracy, specificity, and precision are used to evaluate and compare how well algorithms distinguish legal from illegal transactions.","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},[93,97,101,105,110,114,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]