{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,9]],"date-time":"2026-11-09T09:10:20Z","timestamp":1794215420631,"version":"4.8.0"},"reference-count":24,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T00:00:00Z","timestamp":1759881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>The growing complexity of fraudulent activities presents significant challenges in detecting fraud within financial transactions. Accurate and robust detection methods are essential for minimizing financial losses.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This study evaluates logistic regression, decision tree, and random forest models on real-world credit card datasets, addressing class imbalance and enhancing predictive accuracy. A deep learning model incorporating focal loss was developed to further improve detection performance. The Synthetic Minority Over-Sampling Technique (SMOTE) was applied to mitigate class imbalance, and hyperparameter tuning was conducted to optimize model configurations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Experimental results show that the random forest model achieved the best overall performance, with an accuracy of 99.95%, F1 score of 0.8256, and ROC-AUC of 0.9759. The deep learning model provided the highest precision, demonstrating its potential in minimizing false positives.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>A key novelty of this work is the integration of focal loss within the deep learning framework, enabling the model to focus on hard-to-classify fraudulent transactions. Unlike many prior studies limited to the Kaggle dataset, our approach was validated on both the Kaggle credit card dataset and the PaySim synthetic mobile money dataset, demonstrating robustness and cross-domain generalizability. These findings highlight the effectiveness of combining data preprocessing, resampling techniques, and model optimization for robust fraud detection.<\/jats:p><\/jats:sec>","DOI":"10.3389\/frai.2025.1643292","type":"journal-article","created":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T05:42:52Z","timestamp":1759902172000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":35,"title":["Enhancing credit card fraud detection using traditional and deep learning models with class imbalance mitigation"],"prefix":"10.3389","volume":"8","author":[{"given":"Tahani","family":"Albalawi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samia","family":"Dardouri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,10,8]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"9637","DOI":"10.3390\/app12199637","article-title":"Financial fraud detection based on machine learning: a systematic literature review","volume":"12","author":"Ali","year":"2022","journal-title":"Appl. Sci."},{"key":"ref25","doi-asserted-by":"publisher","first-page":"120","DOI":"10.3390\/computers14040120","article-title":"FraudX AI: An interpretable machine learning framework for credit card fraud detection on imbalanced datasets","volume":"14","author":"Baisholan","year":"2025","journal-title":"Computers."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1016\/j.dss.2010.08.008","article-title":"Data mining for credit card fraud: a comparative study","volume":"50","author":"Bhattacharyya","year":"2011","journal-title":"Decis. Support. Syst."},{"key":"ref3","doi-asserted-by":"publisher","first-page":"189","DOI":"10.4236\/jdaip.2024.122011","article-title":"Real-time fraud detection using machine learning","volume":"12","author":"Borketey","year":"2024","journal-title":"J. Data Anal. Inf. Process."},{"key":"ref4","doi-asserted-by":"publisher","first-page":"1081","DOI":"10.3390\/app15031081","article-title":"Achieving excellence in cyber fraud detection: a hybrid ML+DL ensemble approach for credit cards","volume":"15","author":"Btoush","year":"2025","journal-title":"Appl. Sci."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"012038","DOI":"10.1088\/1742-6596\/1607\/1\/012038","article-title":"A credit card fraud detection model based on random forest","volume":"1607","author":"Chen","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref6","article-title":"Year-over-year developments in financial fraud detection via deep learning: a systematic literature review","author":"Chen","year":"2025"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"1580","DOI":"10.30574\/wjarr.2024.23.3.2811","article-title":"Proactive fraud defense: machine learning\u2019s evolving role in protecting against online fraud","volume":"23","author":"Chy","year":"2024","journal-title":"World J. Adv. Res. Rev."},{"key":"ref8","doi-asserted-by":"crossref","DOI":"10.1145\/3340531.3411903","article-title":"Enhancing graph neural network-based fraud detectors against camouflaged fraudsters","volume-title":"Proceedings of the 29th ACM international conference on information and knowledge management (CIKM\u201920), Ireland","author":"Dou","year":"2020"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ACCESS.2022.3232287","article-title":"Fraud detection in banking data by machine learning techniques","author":"Hashemi","year":"2023","journal-title":"IEEE Access"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"1130","DOI":"10.1057\/s41599-024-03606-0","article-title":"Financial fraud detection through the application of machine learning techniques: a literature review","volume":"11","author":"Hernandez Aros","year":"2024","journal-title":"Humanit. Soc. Sci. Commun."},{"key":"ref11","first-page":"2350044","article-title":"Financial fraud detection: a comparative study of quantum machine learning models","volume-title":"Int. J. Quantum Inf.","author":"Innan","year":"2023"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"103618","DOI":"10.1016\/j.jisa.2023.103618","article-title":"An ensemble learning approach for anomaly detection in credit card data with imbalanced and overlapped classes","volume":"78","author":"Islam","year":"2023","journal-title":"J. Inf. Secur. Appl."},{"key":"ref13","doi-asserted-by":"publisher","first-page":"153","DOI":"10.33650\/jeecom.v5i2.6602","article-title":"Classification of credit card frauds detection using machine learning techniques","volume":"5","author":"Ismail","year":"2023","journal-title":"JEECOM"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"12650","DOI":"10.1016\/j.eswa.2012.05.034","article-title":"Employing transaction aggregation strategy to detect credit card fraud","volume":"39","author":"Jha","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref15","doi-asserted-by":"publisher","first-page":"6","DOI":"10.3390\/bdcc8010006","article-title":"Enhancing credit card fraud detection: an ensemble machine learning approach","volume":"8","author":"Khalid","year":"2024","journal-title":"Big Data Cogn. Comput."},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3751339","article-title":"Comparative analysis for fraud detection using logistic regression, random forest and support vector machine","author":"Kumar","year":"2020"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"15676","DOI":"10.48084\/etasr.7774","article-title":"Comparative assessment of fraudulent financial transactions using the machine learning algorithms decision tree, logistic regression, na\u00efve bayes, k-nearest neighbor, and random forest","volume":"14","author":"Manorom","year":"2024","journal-title":"Eng. Technol. Appl. Sci. Res."},{"key":"ref19","doi-asserted-by":"publisher","first-page":"103858","DOI":"10.1016\/j.jnca.2024.103858","article-title":"Ai-enhanced blockchain technology: a review of advancements and opportunities","volume":"225","author":"Ressi","year":"2024","journal-title":"J. Netw. Comput. Appl."},{"key":"ref20","doi-asserted-by":"publisher","first-page":"es44389-024-02350-5","DOI":"10.7759\/s44389-024-02350-5","article-title":"Fraud detection: a hybrid approach with logistic regression, decision tree, and random forest","volume":"2","author":"Salunke","year":"2025","journal-title":"Cureus J. Comp. Sci."},{"key":"ref21","doi-asserted-by":"publisher","first-page":"02012","DOI":"10.1051\/itmconf\/20235302012","article-title":"Credit card fraud detection using decision tree and random forest","volume":"53","author":"Shah","year":"2023","journal-title":"ITM Web Conf."},{"key":"ref22","doi-asserted-by":"publisher","first-page":"77","DOI":"10.30595\/juita.v10i1.12050","article-title":"Fraud detection using random forest classifier, logistic regression, and gradient boosting classifier algorithms on credit cards","volume":"10","author":"Sopiyan","year":"2022","journal-title":"JUITA"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"172","DOI":"10.3390\/ai4010008","article-title":"Improving classification performance in credit card fraud detection by using new data augmentation","volume":"4","author":"Strelcenia","year":"2023","journal-title":"AI"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1186\/s42400-024-00221-z","article-title":"Securing transactions: a hybrid dependable ensemble machine learning model using IHT-LR and grid search","volume":"7","author":"Talukder","year":"2024","journal-title":"Cybersecurity"}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2025.1643292\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T05:42:53Z","timestamp":1759902173000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2025.1643292\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,8]]},"references-count":24,"alternative-id":["10.3389\/frai.2025.1643292"],"URL":"https:\/\/doi.org\/10.3389\/frai.2025.1643292","relation":{},"ISSN":["2624-8212"],"issn-type":[{"value":"2624-8212","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,8]]},"article-number":"1643292"}}