{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T16:56:00Z","timestamp":1784134560661,"version":"3.55.0"},"reference-count":42,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T00:00:00Z","timestamp":1742860800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science Committee of the Ministry of Education","award":["AP25794007"],"award-info":[{"award-number":["AP25794007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Credit card fraud detection is a critical research area due to the significant financial losses and security risks associated with fraudulent activities. This study presents FraudX AI, an ensemble-based framework addressing the challenges in fraud detection, including imbalanced datasets, interpretability, and scalability. FraudX AI combines random forest and XGBoost as baseline models, integrating their results by averaging probabilities and optimizing thresholds to improve detection performance. The framework was evaluated on the European credit card dataset, maintaining its natural imbalance to reflect real-world conditions. FraudX AI achieved a recall value of 95% and an AUC-PR of 97%, effectively detecting rare fraudulent transactions and minimizing false positives. SHAP (Shapley additive explanations) was applied to interpret model predictions, providing insights into the importance of features in driving decisions. This interpretability enhances usability by offering helpful information to domain experts. Comparative evaluations of eight baseline models, including logistic regression and gradient boosting, as well as existing studies, showed that FraudX AI consistently outperformed these approaches on key metrics. By addressing technical and practical challenges, FraudX AI advances fraud detection systems with its robust performance on imbalanced datasets and its focus on interpretability, offering a scalable and trusted solution for real-world financial applications.<\/jats:p>","DOI":"10.3390\/computers14040120","type":"journal-article","created":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T05:35:03Z","timestamp":1742880903000},"page":"120","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["FraudX AI: An Interpretable Machine Learning Framework for Credit Card Fraud Detection on Imbalanced Datasets"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8134-0466","authenticated-orcid":false,"given":"Nazerke","family":"Baisholan","sequence":"first","affiliation":[{"name":"Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan"},{"name":"Software Engineering Department, International Engineering and Technological University, Almaty 050060, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J. Eric","family":"Dietz","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Technology, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4992-0564","authenticated-orcid":false,"given":"Sergiy","family":"Gnatyuk","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Technology, State University \u201cKyiv Aviation Institute\u201d, 03058 Kyiv, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1470-3706","authenticated-orcid":false,"given":"Mussa","family":"Turdalyuly","sequence":"additional","affiliation":[{"name":"Software Engineering Department, International Engineering and Technological University, Almaty 050060, Kazakhstan"},{"name":"School of Digital Technologies, Narxoz University, Almaty 050035, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric T.","family":"Matson","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Technology, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7375-5998","authenticated-orcid":false,"given":"Karlygash","family":"Baisholanova","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Prajapati, D., Tripathi, A., Mehta, J., Jhaveri, K., and Kelkar, V. (2021, January 3\u20134). Credit Card Fraud Detection Using Machine Learning. Proceedings of the 2021 International Conference on Advances in Computing, Communication, and Control (ICAC3), Mumbai, India.","DOI":"10.1109\/ICAC353642.2021.9697227"},{"key":"ref_2","unstructured":"Juniper Research (2025, January 09). Online Payment Fraud: Market Forecasts, Emerging Threats & Segment Analysis 2023\u20132028, Juniper Research. Available online: https:\/\/www.juniperresearch.com\/research\/fintech-payments\/fraud-identity\/online-payment-fraud-research-report\/."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.ins.2019.05.042","article-title":"Combining unsupervised and supervised learning in credit card fraud detection","volume":"557","author":"Carcillo","year":"2019","journal-title":"J. Inf. Sci."},{"key":"ref_4","first-page":"24","article-title":"Credit Card Fraud Detection System Using Machine Learning","volume":"13","author":"Makolo","year":"2021","journal-title":"Int. J. Inf. Technol. Comput. Sci. (IJITCS)"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"125661","DOI":"10.1016\/j.eswa.2024.125661","article-title":"A distribution-preserving method for resampling combined with LightGBM-LSTM for sequence-wise fraud detection in credit card transactions","volume":"262","author":"Yousefimehr","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_6","first-page":"115","article-title":"Analysis of machine learning and deep learning techniques for credit card fraud detection in class imbalanced datasets","volume":"Volume 1","author":"Kaur","year":"2024","journal-title":"Computational Methods in Science and Technology: Proceedings of the 4th International Conference on Computational Methods in Science & Technology (ICCMST 2024), Mohali, India, 2\u20133 May 2024"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2404","DOI":"10.28991\/ESJ-2024-08-06-016","article-title":"SHAP-Instance Weighted and Anchor Explainable AI: Enhancing XGBoost for Financial Fraud Detection","volume":"8","author":"Thanathamathee","year":"2024","journal-title":"Emerg. Sci. J."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wang, Z., Chen, X., Wu, Y., Jiang, L., Lin, S., and Qiu, G. (2025). A robust and interpretable ensemble machine learning model for predicting healthcare insurance fraud. Sci. Rep., 15.","DOI":"10.1038\/s41598-024-82062-x"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"109810","DOI":"10.1016\/j.engappai.2024.109810","article-title":"A hybrid ensemble model to detect Bitcoin fraudulent transactions","volume":"141","author":"Zhang","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"125674","DOI":"10.1016\/j.eswa.2024.125674","article-title":"Mitigating false negatives in imbalanced datasets: An ensemble approach","volume":"262","author":"Vasconcelos","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"103916","DOI":"10.1016\/j.ipm.2024.103916","article-title":"NNEnsLeG: A novel approach for e-commerce payment fraud detection using ensemble learning and neural networks","volume":"62","author":"Zeng","year":"2025","journal-title":"Inf. Process. Manag."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.jksuci.2022.11.008","article-title":"Credit card fraud detection in the era of disruptive technologies: A systematic review","volume":"35","author":"Cherif","year":"2023","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"96893","DOI":"10.1109\/ACCESS.2024.3426955","article-title":"Deep Learning for Credit Card Fraud Detection: A Review of Algorithms, Challenges, and Solutions","volume":"12","author":"Mienye","year":"2024","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Nobel, S.M.N., Sultana, S., Singha, S.P., Chaki, S., Mahi, M.J.N., Jan, T., Barros, A., and Whaiduzzaman, M. (2024). Unmasking Banking Fraud: Unleashing the Power of Machine Learning and Explainable AI (XAI) on Imbalanced Data. Information, 15.","DOI":"10.3390\/info15060298"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1564","DOI":"10.1109\/TCSS.2022.3232619","article-title":"A Multiperspective Fraud Detection Method for Multiparticipant E-Comerce Transactions","volume":"11","author":"Yu","year":"2024","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_17","first-page":"192","article-title":"A Comparative Study on Machine Learning and Fuzzy Logic-Based Approach for Enhancing Credit Card Fraud Detection","volume":"12","author":"Jayanthi","year":"2024","journal-title":"Int. J. Intell. Syst. Appl. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3034","DOI":"10.1109\/ACCESS.2022.3232287","article-title":"Fraud Detection in Banking Data by Machine Learning Techniques","volume":"11","author":"Hashemi","year":"2023","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mosa, D.T., Sorour, S.E., Abohany, A.A., and Maghraby, F.A. (2024). CCFD: Efficient Credit Card Fraud Detection Using Meta-Heuristic Techniques and Machine Learning Algorithms. Mathematics, 12.","DOI":"10.3390\/math12142250"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e2088","DOI":"10.7717\/peerj-cs.2088","article-title":"Enhancing fraud detection in auto insurance and credit card transactions: A novel approach integrating CNNs and machine learning algorithms","volume":"10","author":"Ming","year":"2024","journal-title":"PeerJ Comput. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Khalid, A.R., Owoh, N., Uthmani, O., Ashawa, M., Osamor, J., and Adejoh, J. (2024). Enhancing Credit Card Fraud Detection: An Ensemble Machine Learning Approach. Big Data Cogn. Comput, 8.","DOI":"10.3390\/bdcc8010006"},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"3536","DOI":"10.1109\/TCSS.2023.3335485","article-title":"ASA-GNN: Adaptive Sampling and Aggregation-Based Graph Neural Network for Transaction Fraud Detection","volume":"11","author":"Tian","year":"2024","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1109\/TCSS.2022.3158318","article-title":"Time-Aware Attention-Based Gated Network for Credit Card Fraud Detection by Extracting Transactional Behaviors","volume":"10","author":"Xie","year":"2023","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6978","DOI":"10.1109\/TASE.2023.3335145","article-title":"A Spatial-Temporal Gated Network for Credit Card Fraud Detection by Learning Transactional Representations","volume":"21","author":"Xie","year":"2024","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5735","DOI":"10.1109\/TNNLS.2022.3208967","article-title":"Learning Transactional Behavioral Representations for Credit Card Fraud Detection","volume":"35","author":"Xie","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_27","unstructured":"(2024, December 10). Worldline and the Machine Learning Group of ULB, Credit Card Fraud Detection Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/mlg-ulb\/creditcardfraud."},{"key":"ref_28","unstructured":"Niu, X., Wang, L., and Yang, X. (2019). A Comparison Study of Credit Card Fraud Detection: Supervised versus Unsupervised. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Uddin, M.F. (2019, January 20\u201321). Addressing accuracy paradox using enhanced weighted performance metric in machine learning. Proceedings of the ITT 2019-Information Technology Trends: Emerging Technologies Blockchain and IoT, Ras Al Khaimah, United Arab Emirates.","DOI":"10.1109\/ITT48889.2019.9075071"},{"key":"ref_30","first-page":"4802","article-title":"Optimizing credit card fraud detection: A deep learning approach to imbalanced datasets","volume":"14","author":"Ndama","year":"2024","journal-title":"Int. J. Electr. Comput. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"119562","DOI":"10.1016\/j.eswa.2023.119562","article-title":"A novel combined approach based on deep Autoencoder and deep classifiers for credit card fraud detection","volume":"217","author":"Fanai","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1631","DOI":"10.1007\/s10614-022-10314-x","article-title":"Performance of Different Machine Learning Algorithms in Detecting Financial Fraud","volume":"62","author":"Alsuwailem","year":"2023","journal-title":"Comput. Econ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Feng, X., and Kim, S.K. (2024). Novel Machine Learning Based Credit Card Fraud Detection System. Mathematics, 12.","DOI":"10.3390\/math12121869"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Awoyemi, J.O., Adetunmbi, A.O., and Oluwadare, S.A. (2017, January 29\u201331). Credit card fraud detection using machine learning techniques: A comparative analysis. Proceedings of the 2017 International Conference on Computing Networking and Informatics (ICCNI), Lagos, Nigeria.","DOI":"10.1109\/ICCNI.2017.8123782"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.neucom.2020.04.078","article-title":"Optimizing Weighted Extreme Learning Machines for imbalanced classification and application to credit card fraud detection","volume":"407","author":"Zhu","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_36","first-page":"451","article-title":"Area under the Precision-Recall Curve: Point Estimates and Confidence Intervals","volume":"Volume 8190","author":"Blockeel","year":"2013","journal-title":"Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2013. Lecture Notes in Computer Science"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"116429","DOI":"10.1016\/j.eswa.2021.116429","article-title":"Financial Fraud: A Review of Anomaly Detection Techniques and Recent Advances","volume":"193","author":"Hilal","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"124979","DOI":"10.1016\/j.eswa.2024.124979","article-title":"Credit card fraud detection based on federated graph learning","volume":"256","author":"Tang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"159316","DOI":"10.1109\/ACCESS.2024.3487212","article-title":"Improved LightGBM for Extremely Imbalanced Data and Application to Credit Card Fraud Detection","volume":"12","author":"Zhao","year":"2024","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.gltp.2021.01.006","article-title":"Credit card fraud detection using artificial neural network","volume":"2","author":"Asha","year":"2021","journal-title":"Glob. Transit. Proc."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"28210","DOI":"10.1109\/ACCESS.2020.2972009","article-title":"A Multiple Classifiers System for Anomaly Detection in Credit Card Data With Unbalanced and Overlapped Classes","volume":"8","author":"Kalid","year":"2020","journal-title":"IEEE Access"},{"key":"ref_42","first-page":"5279","article-title":"Implementation of Machine Learning Techniques to Detect Fraudulent Credit Card Transactions on a Designed Dataset","volume":"101","author":"Baisholan","year":"2023","journal-title":"J. Theor. Appl. Inf. Technol."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/4\/120\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:59:34Z","timestamp":1760029174000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/4\/120"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,25]]},"references-count":42,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["computers14040120"],"URL":"https:\/\/doi.org\/10.3390\/computers14040120","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,25]]}}}