{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T16:22:28Z","timestamp":1781713348257,"version":"3.54.5"},"reference-count":40,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,1,3]],"date-time":"2024-01-03T00:00:00Z","timestamp":1704240000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/bdcc8010006","type":"journal-article","created":{"date-parts":[[2024,1,3]],"date-time":"2024-01-03T11:31:50Z","timestamp":1704281510000},"page":"6","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":178,"title":["Enhancing Credit Card Fraud Detection: An Ensemble Machine Learning Approach"],"prefix":"10.3390","volume":"8","author":[{"given":"Abdul Rehman","family":"Khalid","sequence":"first","affiliation":[{"name":"Department of Cyber Security and Networks, Glasgow Caledonian University, Glasgow G4 0BA, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4840-9345","authenticated-orcid":false,"given":"Nsikak","family":"Owoh","sequence":"additional","affiliation":[{"name":"Department of Cyber Security and Networks, Glasgow Caledonian University, Glasgow G4 0BA, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omair","family":"Uthmani","sequence":"additional","affiliation":[{"name":"Department of Cyber Security and Networks, Glasgow Caledonian University, Glasgow G4 0BA, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1016-0791","authenticated-orcid":false,"given":"Moses","family":"Ashawa","sequence":"additional","affiliation":[{"name":"Department of Cyber Security and Networks, Glasgow Caledonian University, Glasgow G4 0BA, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3739-8521","authenticated-orcid":false,"given":"Jude","family":"Osamor","sequence":"additional","affiliation":[{"name":"Department of Cyber Security and Networks, Glasgow Caledonian University, Glasgow G4 0BA, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Adejoh","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, African University of Science and Technology, Abuja 900107, Nigeria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sahithi, G.L., Roshmi, V., Sameera, Y.V., and Pradeepini, G. (2022, January 28\u201330). Credit Card Fraud Detection using Ensemble Methods in Machine Learning. Proceedings of the 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India.","DOI":"10.1109\/ICOEI53556.2022.9776955"},{"key":"ref_2","unstructured":"Federal Trade Commission (2023, March 11). CSN-Data-Book-2022. no. February 2023, Available online: https:\/\/www.ftc.gov\/system\/files\/ftc_gov\/pdf\/CSN-Data-Book-2022.pdf."},{"key":"ref_3","unstructured":"UK Finance (2023, November 20). Annual Report and Financial Statements 2022. Available online: https:\/\/www.ukfinance.org.uk\/annual-reports."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2575","DOI":"10.1016\/j.procs.2023.01.231","article-title":"Unbalanced Credit Card Fraud Detection Data: A Machine Learning-Oriented Comparative Study of Balancing Techniques","volume":"218","author":"Gupta","year":"2023","journal-title":"Procedia Comput. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Mondal, I.A., Haque, M.E., Hassan, A.-M., and Shatabda, S. (2021, January 18\u201320). Handling imbalanced data for credit card fraud detection. Proceedings of the 2021 24th International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh.","DOI":"10.1109\/ICCIT54785.2021.9689866"},{"key":"ref_6","first-page":"325","article-title":"Class balancing framework for credit card fraud detection based on clustering and similarity-based selection (SBS)","volume":"15","author":"Ahmad","year":"2023","journal-title":"Int. J. Inf. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.procs.2020.06.014","article-title":"Credit card fraud detection using pipelining and ensemble learning","volume":"173","author":"Bagga","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"106883","DOI":"10.1016\/j.asoc.2020.106883","article-title":"Ensemble of deep sequential models for credit card fraud detection","volume":"99","author":"Forough","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1987","DOI":"10.1007\/s13369-021-06147-9","article-title":"Credit card fraud detection by modelling behaviour pattern using hybrid ensemble model","volume":"47","author":"Karthik","year":"2022","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1198\/TECH.2010.07032","article-title":"Statistical methods for fighting financial crimes","volume":"52","author":"Sudjianto","year":"2010","journal-title":"Technometrics"},{"key":"ref_11","first-page":"40","article-title":"Descriptive statistics","volume":"30","author":"Data","year":"2012","journal-title":"Birth"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"102344","DOI":"10.1016\/j.acalib.2021.102344","article-title":"Survey design, sampling, and significance testing: Key issues","volume":"47","author":"Walters","year":"2021","journal-title":"J. Acad. Librariansh."},{"key":"ref_13","unstructured":"Lee, S., and Kim, H.K. (2018, January 23\u201325). Adsas: Comprehensive real-time anomaly detection system. Proceedings of the Information Security Applications: 19th International Conference, WISA 2018, Jeju, Republic of Korea."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"105596","DOI":"10.1016\/j.knosys.2020.105596","article-title":"A review of deep learning with special emphasis on architectures, applications and recent trends","volume":"194","author":"Sengupta","year":"2020","journal-title":"Knowl. Based Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Muppalaneni, N.B., Ma, M., Gurumoorthy, S., Vardhani, P.R., Priyadarshini, Y.I., and Narasimhulu, Y. (2019). Soft Computing and Medical Bioinformatics, Springer.","DOI":"10.1007\/978-981-13-0059-2"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Roy, A., Sun, J., Mahoney, R., Alonzi, L., Adams, S., and Beling, P. (2018, January 27). Deep learning detecting fraud in credit card transactions. Proceedings of the 2018 Systems and Information Engineering Design Symposium (SIEDS), Charlottesville, VA, USA.","DOI":"10.1109\/SIEDS.2018.8374722"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1016\/j.ins.2017.12.030","article-title":"Using generative adversarial networks for improving classification effectiveness in credit card fraud detection","volume":"479","author":"Fiore","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Somvanshi, M., Chavan, P., Tambade, S., and Shinde, S.V. (2016, January 12\u201313). A review of machine learning techniques using decision tree and support vector machine. Proceedings of the 2016 International Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India.","DOI":"10.1109\/ICCUBEA.2016.7860040"},{"key":"ref_19","unstructured":"Shah, R. (2023, November 20). Introduction to k-Nearest Neighbors (kNN) Algorithm. Available online: https:\/\/ai.plainenglish.io\/introduction-to-k-nearest-neighbors-knn-algorithm-e8617a448fa8."},{"key":"ref_20","first-page":"1842","article-title":"Comparative study of K-NN, naive Bayes and decision tree classification techniques","volume":"5","author":"Jadhav","year":"2016","journal-title":"Int. J. Sci. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"14277","DOI":"10.1109\/ACCESS.2018.2806420","article-title":"Credit card fraud detection using AdaBoost and majority voting","volume":"6","author":"Randhawa","year":"2018","journal-title":"IEEE Access"},{"key":"ref_22","first-page":"23","article-title":"Credit card fraud detection using machine learning as data mining technique","volume":"10","author":"Yee","year":"2018","journal-title":"J. Telecommun. Electron. Comput. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Prasad, P.Y., Chowdary, A.S., Bavitha, C., Mounisha, E., and Reethika, C. (2023, January 11\u201313). A Comparison Study of Fraud Detection in Usage of Credit Cards using Machine Learning. Proceedings of the 2023 7th International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India.","DOI":"10.1109\/ICOEI56765.2023.10125838"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Qaddoura, R., and Biltawi, M.M. (December, January 29). Improving Fraud Detection in An Imbalanced Class Distribution Using Different Oversampling Techniques. Proceedings of the 2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI), Zarqa, Jordan.","DOI":"10.1109\/EICEEAI56378.2022.10050500"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Tanouz, D., Subramanian, R.R., Eswar, D., Reddy, G.V.P., Kumar, A.R., and Praneeth, C.H.V.N.M. (2021, January 6\u20138). Credit Card Fraud Detection Using Machine Learning. Proceedings of the 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India.","DOI":"10.1109\/ICICCS51141.2021.9432308"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sailusha, R., Gnaneswar, V., Ramesh, R., and Rao, G.R. (2020, January 13\u201315). Credit Card Fraud Detection Using Machine Learning. Proceedings of the 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India.","DOI":"10.1109\/ICICCS48265.2020.9121114"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.procs.2019.01.007","article-title":"Performance of machine learning techniques in the detection of financial frauds","volume":"148","author":"Sadgali","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Raghavan, P., and El Gayar, N. (2019, January 11\u201312). Fraud Detection using Machine Learning and Deep Learning. Proceedings of the 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), Dubai, United Arab Emirates.","DOI":"10.1109\/ICCIKE47802.2019.9004231"},{"key":"ref_29","first-page":"332","article-title":"Fraud detection using machine learning in e-commerce","volume":"10","author":"Saputra","year":"2019","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_30","first-page":"402","article-title":"A comparative analysis of various credit card fraud detection techniques","volume":"7","author":"Jain","year":"2019","journal-title":"Int. J. Recent Technol. Eng."},{"key":"ref_31","first-page":"8","article-title":"Credit card fraud detection based on machine learning algorithms","volume":"182","author":"Naik","year":"2019","journal-title":"Int. J. Comput. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"16400","DOI":"10.1109\/ACCESS.2022.3148298","article-title":"A neural network ensemble with feature engineering for improved credit card fraud detection","volume":"10","author":"Esenogho","year":"2022","journal-title":"IEEE Access"},{"key":"ref_33","unstructured":"Group, M.L. (2023, November 20). Credit Card Fraud Detection Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/mlg-ulb\/creditcardfraud."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Seiffert, C., Khoshgoftaar, T.M., Van Hulse, J., and Napolitano, A. (2008, January 8\u201311). RUSBoost: Improving classification performance when training data is skewed. Proceedings of the 2008 19th International Conference on Pattern Recognition, Tampa, FL, USA.","DOI":"10.1109\/ICPR.2008.4761297"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1007\/s43681-021-00084-x","article-title":"Putting AI ethics to work: Are the tools fit for purpose?","volume":"2","author":"Ayling","year":"2022","journal-title":"AI Ethics"},{"key":"ref_36","first-page":"598","article-title":"Comparison of ensemble hybrid sampling with bagging and boosting machine learning approach for imbalanced data","volume":"29","author":"Malek","year":"2023","journal-title":"Indones. J. Elec. Eng. Comput. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"301","DOI":"10.32628\/CSEIT195261","article-title":"Credit card fraud detection using random forest algorithm","volume":"5","author":"Niveditha","year":"2019","journal-title":"Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3601","DOI":"10.1021\/acs.chemmater.7b05304","article-title":"Machine learning and energy minimisation approaches for crystal structure predictions: A review and new horizons","volume":"30","author":"Graser","year":"2018","journal-title":"Chem. Mater."},{"key":"ref_39","unstructured":"Kanstr\u00e9n, T. (2023, November 20). A Look at Precision, Recall, and F1-Score. Available online: https:\/\/towardsdatascience.com."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Prusti, D., and Rath, S.K. (2019, January 6\u20138). Fraudulent Transaction Detection in Credit Card by Applying Ensemble Machine Learning Techniques. Proceedings of the 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kanpur, India.","DOI":"10.1109\/ICCCNT45670.2019.8944867"}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/8\/1\/6\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:39:10Z","timestamp":1760103550000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/8\/1\/6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,3]]},"references-count":40,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["bdcc8010006"],"URL":"https:\/\/doi.org\/10.3390\/bdcc8010006","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,3]]}}}