[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120741-en":3,"doc-seo-120741-105":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},120741,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Concept drift and machine learning model for detecting fraudulent transactions in streaming environment - Article","Streaming systems continuously generate data, making it necessary to detect fraudulent transactions quickly to prevent substantial financial losses. This paper presents a machine-learning approach centered on concept drift handling. Using extreme gradient boosting (XGBoost), the method applies four continuous-stream drift detection algorithms and evaluates performance on a credit card dataset and a Twitter dataset with fraud-related social media data. Cross-validation results show improved accuracy, precision, and recall versus traditional models and stronger robustness to concept drift.","International Journal of Electrical and Computer Engineering (IJECE)  \nVol. 13, No. 5, October 2023, pp. 5560~5568  \nISSN: 2088-8708, DOI: 10. 11591/ijece.v13i5 .pp5560-5568 􀂈 5560  \n\n| Concept drift and machine learning model for detecting fraudulent transactions in streaming environment\u003Cbr>Arati Shahapurkar, Rudragoud Patil\u003Cbr>Department of Computer Science and Engineering, KLS Gogte Institute of Technology, Belagavi, India |  |\n| --- | --- |\n| Article Info\u003Cbr>Article history:\u003Cbr>Received Nov 16, 2022 Revised Mar 16, 2023 Accepted Apr 7, 2023\u003Cbr>Keywords:\u003Cbr>Class imbalance Concept drift\u003Cbr>Deep learning Ensemble learning Intrusion detection system Machine learning\u003Cbr>Social network\u003Cbr>Corresponding Author: | In a streaming environment, data is continuously generated and processed inan ongoing manner, and it is necessary to detect fraudulent transactions quickly to prevent significant financial losses. Hence, this paper proposes a machine learning-based approach for detecting fraudulent transactions in a streaming environment, with a focus on addressing concept drift. The approach utilizes the extreme gradient boosting (XGBoost) algorithm. Additionally, the approach employs four algorithms for detecting continuous stream drift. To evaluate the effectiveness of the approach, two datasets are used: a credit card dataset and a Twitter dataset containing financial fraudrelated social media data. The approach is evaluated using cross-validation and the results demonstrate that it outperforms traditional machine learning models in terms of accuracy, precision, and recall, and is more robust to concept drift. The proposed approach can be utilized as a real-time fraud detection system in various industries, including finance, insurance, ande-commerce.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr>\u003Cbr>ABSTRACT |\n| Arati Shahapurkar\u003Cbr>Department of Computer Science and Engineering, KLS Gogte Institute of Technology Belagavi, Karnataka, India\u003Cbr>Email: [asshahapurkar@git.edu](asshahapurkar@git.edu) |  |\n\n1. INTRODUCTION  \nThe growth of the internet significantly aid different organization/fields such as social media, In recent years, credit card fraud has been increasing since there is an increase in the usage of the internet [1] . Nowadays, many people have started utilizing their credit cards for various kinds of transactions and many people fall to scammers which may lead to fraud cases [2] . Credit card fraud refers to a scammer utilizing the user’s credit card number and personal identification number (PIN) or the user ’s stolen credit card for financial transactions from the user’s account without their knowledge. Credit card scams fall under identity theft and have become increasingly common nowadays [3], [4] . There are various ways through which credit card information is usually stolen. Some examples are skimming, dumpster diving, hacking, and phishing [5] . In addition, many scammers are employing Twitter bots to convince misguided users to send money to compromised PayPal as well as Venmo accounts. The bots seem to be launched whenever a genuine user request another one for their payment details, probably obtaining such tweets through a query for terms like PayPal, Venmo, or even other providers. By stealing another user ’s profile photo and coming up with an identical username, they can pass themselves off as them while asking for money from the actual tweeter. Moreover, in the past few years, Twitter spam has gotten progressively worse. Twitter ’s massive user base and the volume of information exchanged there both contribute significantly to the rapid spread of spam [6] . Twitter and the research community have been creating several spam detection systems by utilizing various machine-learning approaches to protect users [7] . However, a recent study found that because the features of  \nspam tweets change over time (data imbalance and concept drift), the current machine learning-based detection methods","cbCaib7kx4UHTDns","https://ap.wps.com/l/cbCaib7kx4UHTDns","pdf",373836,1,9,"English","en",105,"# Introduction\n# Literature Survey","[{\"question\":\"What problem does the paper address in streaming fraud detection?\",\"answer\":\"It addresses the need for fast detection under data that changes over time, focusing specifically on concept drift and class imbalance, which make traditional models less accurate.\"},{\"question\":\"Which machine learning method is proposed for detecting fraudulent transactions?\",\"answer\":\"The approach uses extreme gradient boosting (XGBoost) and incorporates four algorithms to detect continuous stream drift.\"},{\"question\":\"How is the proposed method evaluated and what are the results?\",\"answer\":\"The paper evaluates the approach with cross-validation on a credit card dataset and a Twitter dataset containing finance-fraud-related social media data. The results indicate better accuracy, precision, recall, and improved robustness to concept drift compared with traditional machine learning models.\"}]","Concept drift and machine learning model for detecting fraudulent transactions in streaming environment - Article | PDF",1785731792,23,{"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},"concept-drift-and-machine-learning-model-for-detecting-fraudulent-transactions-in-streaming-environment-article","",{"@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/concept-drift-and-machine-learning-model-for-detecting-fraudulent-transactions-in-streaming-environment-article/120741/",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","2026-08-03",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},"What problem does the paper address in streaming fraud detection?","Question",{"text":75,"@type":76},"It addresses the need for fast detection under data that changes over time, focusing specifically on concept drift and class imbalance, which make traditional models less accurate.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning method is proposed for detecting fraudulent transactions?",{"text":80,"@type":76},"The approach uses extreme gradient boosting (XGBoost) and incorporates four algorithms to detect continuous stream drift.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the proposed method evaluated and what are the results?",{"text":84,"@type":76},"The paper evaluates the approach with cross-validation on a credit card dataset and a Twitter dataset containing finance-fraud-related social media data. The results indicate better accuracy, precision, recall, and improved robustness to concept drift compared with traditional machine learning models.","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},[93,97,101,105,110,115,120,123,127,130,134],{"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":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":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"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"]