With the development of technology, the internet and eCommerce online payment has become an essential mode of payment. Nowadays, credit card payment is a convenient mode of payment online as well as offline transactions. As online credit card payment increases, fraud transactions are likewise increasing day by day. Increasing fraud transactions in the online payment system became a more significant challenge for banks, companies, and researchers. Therefore, it is essential to have an efficient methodology to detect fraud transactions while payment has completed via credit card. Although many traditional approaches are already available for fraud transaction prediction, however, existing methods lack accuracy, and it can be increased by ensemble techniques such as XGBoost. In this paper, we use an ensemble approach that is XGBoost (eXtreme Gradient Boosting) for credit card fraud prediction. The results are compared with existing machine learning approaches.<\/p>","DOI":"10.4018\/ijirr.299940","type":"journal-article","created":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T02:09:30Z","timestamp":1651716570000},"page":"1-17","source":"Crossref","is-referenced-by-count":6,"title":["Credit Card Fraud Prediction Using XGBoost"],"prefix":"10.4018","volume":"12","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-7566-0703","authenticated-orcid":true,"given":"Krishna Kumar","family":"Mohbey","sequence":"first","affiliation":[{"name":"Central University of Rajasthan, India"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-2409-7172","authenticated-orcid":true,"given":"Mohammad Zubair","family":"Khan","sequence":"additional","affiliation":[{"name":"Taibah University, Saudi Arabia"}]},{"given":"Ajay","family":"Indian","sequence":"additional","affiliation":[{"name":"Central University of Rajasthan, India"}]}],"member":"2432","reference":[{"doi-asserted-by":"publisher","key":"IJIRR.299940-0","DOI":"10.3390\/risks6020038"},{"doi-asserted-by":"publisher","key":"IJIRR.299940-1","DOI":"10.1016\/S0378-4266(03)00202-4"},{"doi-asserted-by":"publisher","key":"IJIRR.299940-2","DOI":"10.1016\/j.dss.2010.08.008"},{"unstructured":"Bolton, R. J., & Hand, D. J. (2001). Unsupervised profiling methods for fraud detection. Credit Scoring and Credit Control, 7, 235-255.","key":"IJIRR.299940-3"},{"doi-asserted-by":"publisher","key":"IJIRR.299940-4","DOI":"10.1023\/A:1010933404324"},{"doi-asserted-by":"publisher","key":"IJIRR.299940-5","DOI":"10.1016\/j.jbankfin.2016.07.015"},{"doi-asserted-by":"publisher","key":"IJIRR.299940-6","DOI":"10.1016\/j.inffus.2017.09.005"},{"doi-asserted-by":"publisher","key":"IJIRR.299940-7","DOI":"10.1109\/5254.809570"},{"doi-asserted-by":"publisher","key":"IJIRR.299940-8","DOI":"10.1093\/rof\/8.4.537"},{"doi-asserted-by":"publisher","key":"IJIRR.299940-9","DOI":"10.1145\/2939672.2939785"},{"doi-asserted-by":"publisher","key":"IJIRR.299940-10","DOI":"10.1007\/BF00994018"},{"issue":"8","key":"IJIRR.299940-11","doi-asserted-by":"crossref","first-page":"3784","DOI":"10.1109\/TNNLS.2017.2736643","article-title":"Credit card fraud detection: A realistic modeling and a novel learning strategy.","volume":"29","author":"A.Dal Pozzolo","year":"2017","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"doi-asserted-by":"publisher","key":"IJIRR.299940-12","DOI":"10.1109\/SSCI.2015.33"},{"issue":"6","key":"IJIRR.299940-13","first-page":"262","article-title":"Performance evaluation of credit card fraud transactions using boosting algorithms.","volume":"10","author":"K.Divakar","year":"2019","journal-title":"Int. J. Electron. Commun. Comput. Eng. IJECCE"},{"doi-asserted-by":"publisher","key":"IJIRR.299940-14","DOI":"10.1016\/j.eswa.2011.01.064"},{"doi-asserted-by":"publisher","key":"IJIRR.299940-15","DOI":"10.1016\/j.eswa.2018.01.037"},{"doi-asserted-by":"publisher","key":"IJIRR.299940-16","DOI":"10.5455\/jjcit.71-1546924503"},{"unstructured":"Maes, S., Tuyls, K., Vanschoenwinkel, B., & Manderick, B. (2002, January). Credit card fraud detection using Bayesian and neural networks. In Proceedings of the 1st international naiso congress on neuro fuzzy technologies (pp. 261-270). Academic Press.","key":"IJIRR.299940-17"},{"doi-asserted-by":"publisher","key":"IJIRR.299940-18","DOI":"10.1007\/s11277-021-08283-9"},{"doi-asserted-by":"publisher","key":"IJIRR.299940-19","DOI":"10.1016\/j.ijforecast.2011.07.005"},{"issue":"5","key":"IJIRR.299940-20","first-page":"1647","article-title":"Classification techniques in education domain.","volume":"2","author":"B.Nithyassik","year":"2010","journal-title":"International Journal on Computer Science and Engineering"},{"unstructured":"Niu, X., Wang, L., & Yang, X. (2019). A comparison study of credit card fraud detection: Supervised versus unsupervised. arXiv preprint arXiv:1904.10604.","key":"IJIRR.299940-21"},{"unstructured":"Odegua, R. (2020). Predicting Bank Loan Default with Extreme Gradient Boosting. arXiv preprint arXiv:2002.02011.","key":"IJIRR.299940-22"},{"doi-asserted-by":"publisher","key":"IJIRR.299940-23","DOI":"10.2174\/2666255813999200721004720"},{"unstructured":"Petropoulos, A., Siakoulis, V., Stavroulakis, E., & Klamargias, A. (2019). A robust machine learning approach for credit risk analysis of large loan level datasets using deep learning and extreme Gradient boosting. IFC Bulletins Chapters, 49.","key":"IJIRR.299940-24"},{"doi-asserted-by":"publisher","key":"IJIRR.299940-25","DOI":"10.1109\/ACCESS.2018.2806420"},{"doi-asserted-by":"publisher","key":"IJIRR.299940-26","DOI":"10.1109\/COMPSAC.2018.00114"},{"doi-asserted-by":"publisher","key":"IJIRR.299940-27","DOI":"10.1109\/SIEDS.2018.8374722"},{"doi-asserted-by":"publisher","key":"IJIRR.299940-28","DOI":"10.1016\/j.eswa.2013.05.021"},{"doi-asserted-by":"publisher","key":"IJIRR.299940-29","DOI":"10.1007\/s10479-021-04149-2"},{"unstructured":"Shakya, R. (2018). Application of Machine Learning Techniques in Credit Card Fraud Detection (Doctoral dissertation). University of Nevada, Las Vegas, NV.","key":"IJIRR.299940-30"},{"doi-asserted-by":"publisher","key":"IJIRR.299940-31","DOI":"10.1109\/TDSC.2007.70228"},{"doi-asserted-by":"crossref","unstructured":"Tripathi, D., Edla, D. R., Bablani, A., Shukla, A. K., & Reddy, B. R. (2021). Experimental analysis of machine learning methods for credit score classification. Progress in Artificial Intelligence, 1-27.","key":"IJIRR.299940-32","DOI":"10.1007\/s13748-021-00238-2"},{"doi-asserted-by":"publisher","key":"IJIRR.299940-33","DOI":"10.1007\/0-387-34239-7"},{"doi-asserted-by":"publisher","key":"IJIRR.299940-34","DOI":"10.1109\/72.788640"},{"doi-asserted-by":"publisher","key":"IJIRR.299940-35","DOI":"10.1109\/TIP.2015.2403231"},{"doi-asserted-by":"publisher","key":"IJIRR.299940-36","DOI":"10.1016\/j.ins.2017.04.015"},{"unstructured":"Zojaji, Z., Atani, R. E., & Monadjemi, A. H. (2016). A survey of credit card fraud detection techniques: data and technique oriented perspective. arXiv preprint arXiv:1611.06439.","key":"IJIRR.299940-37"}],"container-title":["International Journal of Information Retrieval Research"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=299940","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T20:42:36Z","timestamp":1727124156000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJIRR.299940"}},"subtitle":["An Ensemble Learning Approach"],"short-title":[],"issued":{"date-parts":[[2022,7,8]]},"references-count":38,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,4]]}},"URL":"https:\/\/doi.org\/10.4018\/ijirr.299940","relation":{},"ISSN":["2155-6377","2155-6385"],"issn-type":[{"type":"print","value":"2155-6377"},{"type":"electronic","value":"2155-6385"}],"subject":[],"published":{"date-parts":[[2022,7,8]]}}}