[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118883-en":3,"doc-seo-118883-105":30,"detail-sidebar-cat-0-en-105":92},{"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},118883,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Enhancing Credit Card Fraud Detection - An Ensemble Machine Learning Approach","Escalating credit card fraud requires robust and efficient detection systems, motivating the use of machine learning with ensemble methods. The study reviews existing fraud detection approaches and highlights persistent limitations, including data imbalance, concept drift, false positives/negatives, limited generalisability, and difficulties with real-time processing. To mitigate these issues, an ensemble model combines SVM, KNN, Random Forest, Bagging, and Boosting, using under-sampling and SMOTE to address imbalanced credit card datasets. Evaluation on European transaction records assesses preprocessing, feature engineering, model selection, and performance across accuracy, precision, recall, and F1-score.","Enhancing credit card fraud detection: an ensemble machine learning approach  \nRehman Khalid, Abdul ; Owoh, Nsikak; Uthmani, Omair; Ashawa, Moses; Osamor, Jude; Adejoh, John  \nPublished in:  \nBig Data and Cognitive Computing  \nDOI:  \n10.3390/bdcc8010006  \nPublication date:  \n2024  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication in ResearchOnline  \nCitation for published version (Harvard):  \nRehman Khalid, A, Owoh, N, Uthmani, O, Ashawa, M, Osamor, J & Adejoh, J 2024, 'Enhancing credit card fraud detection: an ensemble machine learning approach', Big Data and Cognitive Computing, vol. 8, no. 1.  \n[https://doi.org/10.3390/bdcc8010006](https://doi.org/10.3390/bdcc8010006)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nIf you believe that this document breaches copyright please view our takedown policy at [https://edshare.gcu.ac.uk/id/eprint/5179 for details](https://edshare.gcu.ac.uk/id/eprint/5179 for details)[ ](https://edshare.gcu.ac.uk/id/eprint/5179 for details)[of how to contact us.](of how to contact us.)  \nDownload date: 12. Jan. 2024  \nArticle  \nEnhancing Credit Card Fraud Detection: An Ensemble Machine Learning Approach  \nAbdul Rehman Khalid 1, Nsikak Owoh 1, *, Omair Uthmani 1, Moses Ashawa 1, Jude Osamor 1 and John Adejoh 2  \nCitation: Khalid, A.R.; Owoh, N.; Uthmani, O.; Ashawa, M.; Osamor, J.; Adejoh, J. Enhancing Credit Card Fraud Detection: An Ensemble Machine Learning Approach. Big Data Cogn. Comput. 2024, 8, 6. [https://](https://)[ ](https://)[doi.org/10.3390/bdcc8010006](doi.org/10.3390/bdcc8010006)  \nAcademic Editor: Domenico Ursino  \nReceived: 21 November 2023  \nRevised: 22 December 2023  \nAccepted: 28 December 2023  \nPublished: 3 January 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Cyber Security and Networks, Glasgow Caledonian University, Glasgow G4 0BA, UK; [akhali301@caledonian.ac.uk](akhali301@caledonian.ac.uk) (A.R.K.)  \n2 Department of Software Engineering, African University of Science and Technology, Abuja 900107, Nigeria; [ajohn@staff.aust.edu.ng](ajohn@staff.aust.edu.ng)  \n* [Correspondence: nsikak.owoh@gcu.ac.uk](Correspondence: nsikak.owoh@gcu.ac.uk)  \nAbstract: In the era of digital advancements, the escalation of credit card fraud necessitates the development of robust and efficient fraud detection systems. This paper delves into the application of machine learning models, specifically focusing on ensemble methods, to enhance credit card fraud detection. Through an extensive review of existing literature, we identified limitations in current fraud detection technologies, including issues like data imbalance, concept drift, false positives/negatives, limited generalisability, and challenges in real-time processing. To address some of these shortcomings, we propose a novel ensemble model that integrates a Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Bagging, and Boosting classifiers. This ensemble model tackles the dataset imbalance problem associated with most credit card datasets by implementing under-sampling and the Synthetic Over-sampling Technique (SMOTE) on some machine learning algorithms. The evaluation of the model utilises a dataset comprising transaction records from European credit card holders, providing a realistic scenario for assessment. The methodology of the proposed model encompasses data pre-processing, ","cbCaipiXOurw0dLj","https://ap.wps.com/l/cbCaipiXOurw0dLj","pdf",5184491,1,28,"English","en",105,"# Abstract\n# Introduction\n## Problem context and trends\n# Proposed ensemble approach\n## Model components and resampling strategy\n# Methodology and evaluation\n## Pre-processing, feature engineering, and metrics\n# Results and discussion\n## Comparison with baseline and individual classifiers\n# Conclusion and future work","[{\"question\":\"What problem does the paper address in credit card fraud detection?\",\"answer\":\"It addresses the growing challenge of detecting credit card fraud effectively despite issues such as data imbalance, concept drift, and false positives/negatives, especially in real-time settings.\"},{\"question\":\"How does the proposed ensemble model improve detection performance?\",\"answer\":\"The model integrates SVM, KNN, Random Forest, Bagging, and Boosting, and applies under-sampling and SMOTE to reduce the impact of imbalanced datasets.\"},{\"question\":\"What metrics and dataset are used to evaluate the model?\",\"answer\":\"The evaluation uses a European credit card transaction dataset and compares performance using accuracy, precision, recall, and F1-score, showing superior results for the ensemble.\"}]","Enhancing Credit Card Fraud Detection - 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