{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T18:28:39Z","timestamp":1780597719851,"version":"3.54.1"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T00:00:00Z","timestamp":1704758400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T00:00:00Z","timestamp":1704758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-023-02559-6","type":"journal-article","created":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T11:03:03Z","timestamp":1704798183000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Credit Card Fraud Detection: Addressing Imbalanced Datasets with a Multi-phase Approach"],"prefix":"10.1007","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1475-7675","authenticated-orcid":false,"given":"Fatima Zohra","family":"El Hlouli","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jamal","family":"Riffi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed Adnane","family":"Mahraz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Yahyaouy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khalid","family":"El Fazazy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamid","family":"Tairi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,9]]},"reference":[{"issue":"1","key":"2559_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-021-00978-x","volume":"3","author":"YK Gupta","year":"2022","unstructured":"Gupta YK, Jeswani G, Pinto O. M-commerce offline payment. SN Comput Sci. 2022;3(1):1\u201311. https:\/\/doi.org\/10.1007\/s42979-021-00978-x.","journal-title":"SN Comput Sci"},{"issue":"3","key":"2559_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-021-00539-2","volume":"2","author":"S Ingole","year":"2021","unstructured":"Ingole S, Kumar A, Prusti D, Rath SK. Service-based credit card fraud detection using oracle SOA suite. SN Comput Sci. 2021;2(3):1\u20139. https:\/\/doi.org\/10.1007\/s42979-021-00539-2.","journal-title":"SN Comput Sci"},{"key":"2559_CR3","unstructured":"\u201cNilson Report\u201d, no. 1209. 2021 [Online]. Available: https:\/\/nilsonreport.com\/upload\/content_promo\/NilsonReport_Issue1209.pdf."},{"key":"2559_CR4","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.ins.2019.07.070","volume":"505","author":"D Elreedy","year":"2019","unstructured":"Elreedy D, Atiya AF. A comprehensive analysis of synthetic minority oversampling technique (SMOTE) for handling class imbalance. Inf Sci (NY). 2019;505:32\u201364. https:\/\/doi.org\/10.1016\/j.ins.2019.07.070.","journal-title":"Inf Sci (NY)"},{"issue":"3","key":"2559_CR5","doi-asserted-by":"publisher","first-page":"423","DOI":"10.32890\/JICT2021.20.3.6","volume":"20","author":"AY Taha","year":"2021","unstructured":"Taha AY, Tiun S, Rahman AHA, Sabah A. Multilabel over-sampling and under-sampling with class alignment for imbalanced multilabel text classification. J Inf Commun Technol. 2021;20(3):423\u201356. https:\/\/doi.org\/10.32890\/JICT2021.20.3.6.","journal-title":"J Inf Commun Technol"},{"key":"2559_CR6","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1601\/5\/052016","author":"C Meng","year":"2020","unstructured":"Meng C, Zhou L, Liu B. A case study in credit fraud detection with SMOTE and XGboost. J Phys Conf Ser. 2020. https:\/\/doi.org\/10.1088\/1742-6596\/1601\/5\/052016.","journal-title":"J Phys Conf Ser"},{"key":"2559_CR7","doi-asserted-by":"publisher","unstructured":"Yu X, Li X, Dong Y, Zheng R. A deep neural network algorithm for detecting credit card fraud. In: Proc.\u20142020 Int. Conf. Big Data, Artif. Intell. Internet Things Eng. ICBAIE; 2020. p. 181\u20133. https:\/\/doi.org\/10.1109\/ICBAIE49996.2020.00045.","DOI":"10.1109\/ICBAIE49996.2020.00045"},{"issue":"2","key":"2559_CR8","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1080\/23080477.2020.1783491","volume":"8","author":"M Arya","year":"2020","unstructured":"Arya M, Sastry HG. DEAL\u2014\u2018Deep Ensemble ALgorithm\u2019 framework for credit card fraud detection in real-time data stream with Google TensorFlow. Smart Sci. 2020;8(2):71\u201383. https:\/\/doi.org\/10.1080\/23080477.2020.1783491.","journal-title":"Smart Sci"},{"key":"2559_CR9","doi-asserted-by":"publisher","unstructured":"Salazar A, Safont G, Vergara L. Semi-supervised learning for imbalanced classification of credit card transaction. In: Proc. Int. Jt. Conf. Neural Networks, vol. 2018-July, p. 1\u20137, 2018. https:\/\/doi.org\/10.1109\/IJCNN.2018.8489755.","DOI":"10.1109\/IJCNN.2018.8489755"},{"issue":"1","key":"2559_CR10","doi-asserted-by":"publisher","first-page":"18","DOI":"10.14569\/IJACSA.2018.090103","volume":"9","author":"A Pumsirirat","year":"2018","unstructured":"Pumsirirat A, Yan L. Credit card fraud detection using deep learning based on auto-encoder and restricted Boltzmann machine. Int J Adv Comput Sci Appl. 2018;9(1):18\u201325. https:\/\/doi.org\/10.14569\/IJACSA.2018.090103.","journal-title":"Int J Adv Comput Sci Appl"},{"key":"2559_CR11","unstructured":"Najem SM, Kadhem S. A Survey On Fraud Detection Techniques in E-Commerce. 2021;1(1)."},{"key":"2559_CR12","doi-asserted-by":"publisher","unstructured":"Roy A, Sun J, Mahoney R, Alonzi L, Adams S, Beling P. Deep learning detecting fraud in credit card transactions. In: 2018 Systems and Information Engineering Design Symposium (SIEDS), 2018, p. 129\u201334. https:\/\/doi.org\/10.1109\/sieds.2018.8374722.","DOI":"10.1109\/sieds.2018.8374722"},{"key":"2559_CR13","doi-asserted-by":"publisher","unstructured":"El Hlouli FZ, Riffi J, Mahraz MA, El Yahyaouy A, Tairi H. Credit card fraud detection based on multilayer perceptron and extreme learning machine architectures. In: 2020 Int. Conf. Intell. Syst. Comput. Vision, ISCV; 2020. https:\/\/doi.org\/10.1109\/ISCV49265.2020.9204185.","DOI":"10.1109\/ISCV49265.2020.9204185"},{"key":"2559_CR14","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.neucom.2020.04.078","volume":"407","author":"H Zhu","year":"2020","unstructured":"Zhu H, Liu G, Zhou M, Xie Y, Abusorrah A. Neurocomputing optimizing weighted extreme learning machines for imbalanced classification and application to credit card fraud detection. Neurocomputing. 2020;407:50\u201362. https:\/\/doi.org\/10.1016\/j.neucom.2020.04.078.","journal-title":"Neurocomputing"},{"issue":"1","key":"2559_CR15","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.gltp.2021.01.006","volume":"2","author":"A Rb","year":"2021","unstructured":"Rb A, Kr SK. Credit card fraud detection using artificial neural network. Glob Transit Proc. 2021;2(1):35\u201341. https:\/\/doi.org\/10.1016\/j.gltp.2021.01.006.","journal-title":"Glob Transit Proc"},{"key":"2559_CR16","doi-asserted-by":"publisher","DOI":"10.1007\/s41870-020-00430-y","author":"F Itoo","year":"2020","unstructured":"Itoo F, Meenakshi, Singh S. Comparison and analysis of logistic regression, Na\u0131ve Bayes and KNN machine learning algorithms for credit card fraud detection. Int J Inf Technol. 2020. https:\/\/doi.org\/10.1007\/s41870-020-00430-y.","journal-title":"Int J Inf Technol"},{"key":"2559_CR17","doi-asserted-by":"publisher","unstructured":"Xuan S, Liu G, Li Z, Zheng L, Wang S, Jiang C. Random forest for credit card fraud detection. In: ICNSC 2018\u201415th IEEE Int. Conf. Networking, Sens. Control, p. 1\u20136, 2018. https:\/\/doi.org\/10.1109\/ICNSC.2018.8361343.","DOI":"10.1109\/ICNSC.2018.8361343"},{"key":"2559_CR18","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1016\/j.eswa.2018.01.037","volume":"100","author":"J Jurgovsky","year":"2018","unstructured":"Jurgovsky J, et al. Sequence classification for credit-card fraud detection. Expert Syst Appl. 2018;100:234\u201345. https:\/\/doi.org\/10.1016\/j.eswa.2018.01.037.","journal-title":"Expert Syst Appl"},{"key":"2559_CR19","doi-asserted-by":"publisher","unstructured":"Fu K, Cheng D, Tu Y, B LZ. Credit card fraud detection using convolutional neural networks. 2016:483\u2013490. https:\/\/doi.org\/10.1007\/978-3-319-46675-0.","DOI":"10.1007\/978-3-319-46675-0"},{"key":"2559_CR20","doi-asserted-by":"publisher","unstructured":"Devi D, Biswas SK, Purkayastha B. A review on solution to class imbalance problem: undersampling approaches. In: 2020 Int. Conf. Comput. Perform. Eval. ComPE; 2020. p. 626\u201331. https:\/\/doi.org\/10.1109\/ComPE49325.2020.9200087.","DOI":"10.1109\/ComPE49325.2020.9200087"},{"issue":"4","key":"2559_CR21","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1080\/0952813X.2021.1907795","volume":"34","author":"A Singh","year":"2022","unstructured":"Singh A, Ranjan RK, Tiwari A. Credit card fraud detection under extreme imbalanced data: a comparative study of data-level algorithms. J Exp Theor Artif Intell. 2022;34(4):571\u201398. https:\/\/doi.org\/10.1080\/0952813X.2021.1907795.","journal-title":"J Exp Theor Artif Intell"},{"issue":"February","key":"2559_CR22","doi-asserted-by":"publisher","first-page":"16400","DOI":"10.1109\/ACCESS.2022.3148298","volume":"10","author":"E Esenogho","year":"2022","unstructured":"Esenogho E, Mienye ID, Swart TG, Aruleba K, Obaido G. A neural network ensemble with feature engineering for improved credit card fraud detection. IEEE Access. 2022;10(February):16400\u20137. https:\/\/doi.org\/10.1109\/ACCESS.2022.3148298.","journal-title":"IEEE Access"},{"key":"2559_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.9734\/jamcs\/2019\/v33i530192","volume":"33","author":"MA Al-shabi","year":"2019","unstructured":"Al-shabi MA. Credit card fraud detection using autoencoder model in unbalanced datasets. J Adv Math Comput Sci. 2019;33:1\u201316. https:\/\/doi.org\/10.9734\/jamcs\/2019\/v33i530192.","journal-title":"J Adv Math Comput Sci"},{"issue":"24","key":"2559_CR24","doi-asserted-by":"publisher","first-page":"108","DOI":"10.3991\/IJIM.V15I24.27355","volume":"15","author":"AH Almuteer","year":"2021","unstructured":"Almuteer AH, Aloufi AA, Alrashidi WO, Alshobaili JF, Ibrahim DM. Detecting credit card fraud using machine learning. Int J Interact Mob Technol. 2021;15(24):108\u201322. https:\/\/doi.org\/10.3991\/IJIM.V15I24.27355.","journal-title":"Int J Interact Mob Technol"},{"key":"2559_CR25","doi-asserted-by":"publisher","first-page":"14277","DOI":"10.1109\/ACCESS.2018.2806420","volume":"6","author":"K Randhawa","year":"2018","unstructured":"Randhawa K, Loo CK, Member S, Seera M, Lim CP, Nandi AK. Credit card fraud detection using AdaBoost and majority voting. IEEE Access. 2018;6:14277\u201384. https:\/\/doi.org\/10.1109\/ACCESS.2018.2806420.","journal-title":"IEEE Access"},{"key":"2559_CR26","doi-asserted-by":"publisher","first-page":"25579","DOI":"10.1109\/ACCESS.2020.2971354","volume":"8","author":"AA Taha","year":"2020","unstructured":"Taha AA, Malebary SJ. An intelligent approach to credit card fraud detection using an optimized light gradient boosting machine. IEEE Access. 2020;8:25579\u201387. https:\/\/doi.org\/10.1109\/ACCESS.2020.2971354.","journal-title":"IEEE Access"},{"key":"2559_CR27","unstructured":"Zou J, Zhang J, Jiang P. Credit card fraud detection using autoencoder neural network. 2019 [Online]. Available: http:\/\/arxiv.org\/abs\/1908.11553."},{"key":"2559_CR28","doi-asserted-by":"publisher","DOI":"10.1007\/s10796-022-10346-6","author":"P Hajek","year":"2022","unstructured":"Hajek P, Abedin MZ, Sivarajah U. Fraud detection in mobile payment systems using an XGBoost-based framework. Inf Syst Front. 2022. https:\/\/doi.org\/10.1007\/s10796-022-10346-6.","journal-title":"Inf Syst Front"},{"key":"2559_CR29","doi-asserted-by":"publisher","unstructured":"Cochrane, et al. Pattern analysis for transaction fraud detection. In: 2021 IEEE 11th Annu. Comput. Commun. Work. Conf. CCWC; 2021. p. 283\u20139. https:\/\/doi.org\/10.1109\/CCWC51732.2021.9376045.","DOI":"10.1109\/CCWC51732.2021.9376045"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-023-02559-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-023-02559-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-023-02559-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T11:04:43Z","timestamp":1704798283000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-023-02559-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,9]]},"references-count":29,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["2559"],"URL":"https:\/\/doi.org\/10.1007\/s42979-023-02559-6","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,9]]},"assertion":[{"value":"26 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"173"}}