[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117018-en":3,"doc-seo-117018-105":29,"detail-sidebar-cat-0-en-105":90},{"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":13,"seo_description":14,"update_tm":27,"read_time":28},117018,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Enhancing Credit Card Fraud Detection - An Ensemble Machine Learning Approach","Credit card fraud continues to accelerate alongside growing card usage, creating urgent demand for accurate and efficient fraud detection. This paper surveys key limitations in existing systems, including data imbalance, concept drift, misclassification via false positives and false negatives, restricted generalisability, and difficulties with real-time processing. To mitigate these issues, it proposes an ensemble model that combines SVM, KNN, Random Forest, Bagging, and Boosting, using under-sampling and SMOTE to address skewed class distributions. Evaluation on European transaction records shows stronger performance than traditional single-model approaches across accuracy, precision, recall, and F1-score.","Article  \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, feature engineering, model selection, and evaluation, with Google Colab computational capabilities facilitating efficient model training and testing. Comparative analysis between the proposed ensemble model, traditional machine learning methods, and individual classifiers reveals the superior performance of the ensemble in mitigating challenges associated with credit card fraud detection. Across accuracy, precision, recall, and F1-score metrics, the ensemble outperforms existing models. This paper underscores the efficacy of ensemble methods as a valuable tool in the battle against fraudulent transactions. The findings presented lay the groundwork for future advancements in the development of more resilient and adaptive fraud detection systems, which will become crucial as credit card fraud techniques continue to evolve.  \nKeywords: credit card fraud detection; ensemble model; machine learning; data imbalance; Synthetic Minority Over-sampling Technique; deep learning  \n1. Introduction  \nFraudulent activities are on the rise within the financial sector, with an escalating trend observed in credit card fraud. The incidence of credit card fraud is expanding swiftly in tandem with the increasing daily usage of credit cards [1] . The Federal Trade Commission (FTC) report underscores the severity of the issue, noting that 2021 marked the most challenging y","cbCaicxp6BZNcGjk","https://ap.wps.com/l/cbCaicxp6BZNcGjk","pdf",5133552,1,27,"English","en",105,"# Abstract\n# Introduction\n## Background and scale of credit card fraud\n# Proposed Ensemble Approach\n## Model components and class-imbalance handling\n# Experimental Setup and Evaluation\n## Dataset description and metrics\n# Results and Discussion\n## Comparison with baselines","[{\"question\":\"What problem does the paper target in credit card fraud detection?\",\"answer\":\"It targets the growing challenge of credit card fraud and the weaknesses of current detection methods, especially data imbalance and classification errors.\"},{\"question\":\"How does the proposed method improve detection performance?\",\"answer\":\"It builds an ensemble combining SVM, KNN, Random Forest, Bagging, and Boosting, and applies under-sampling plus SMOTE to address imbalanced datasets.\"},{\"question\":\"How is the model evaluated and what results are reported?\",\"answer\":\"The paper evaluates the ensemble using transaction records from European credit card holders and compares performance with traditional and individual classifiers using accuracy, precision, recall, and F1-score, where the ensemble performs 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problem does the paper target in credit card fraud detection?","Question",{"text":74,"@type":75},"It targets the growing challenge of credit card fraud and the weaknesses of current detection methods, especially data imbalance and classification errors.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the proposed method improve detection performance?",{"text":79,"@type":75},"It builds an ensemble combining SVM, KNN, Random Forest, Bagging, and Boosting, and applies under-sampling plus SMOTE to address imbalanced datasets.",{"name":81,"@type":72,"acceptedAnswer":82},"How is the model evaluated and what results are reported?",{"text":83,"@type":75},"The paper evaluates the ensemble using transaction records from European credit card holders and compares performance with traditional and individual classifiers using accuracy, precision, recall, and F1-score, where the ensemble performs 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