Federated Deep Learning and Explainable AI for Real-Time Credit Card Fraud Detection in Highly Imbalanced Transaction Streams

Federated Deep Learning and Explainable AI for Real-Time Credit Card Fraud Detection in Highly Imbalanced Transaction Streams | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Federated Deep Learning and Explainable AI for Real-Time Credit Card Fraud Detection in Highly Imbalanced Transaction Streams Sundaravadivel P, Augustian Isaac R This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7587282/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The fraud detection systems using credit cards available today would need to contend, on the one hand, with the dynamically evolving cyber-fraud tactics and, on the other hand, the high-security data protection compliance regulations, as well as the volume and dynamics of the streaming records of transactions. The existing ensemble learning systems (Random Forest + SMOTE) are extended into the new Federated Deep Learning (FDL) and into the Explainable AI (XAI) leading to the fraud detection in real-time. The proposed framework is a combination of the BiLSTM-CNN hybrid model to track temporal as well as spatial patterns within the transactions and, in the meantime, preserve the data locality by means of a federated aggregation. Extreme class imbalance is one of the challenging problems in machine learning application and, hence an oversampling process with the help of the Generative Adversarial Network (GAN) is proposed to learn the minority distribution of frauds better in comparison to a conventional SMOTE. Moreover, layers of explanability like SHAP and LIME are implemented to have explainable risk scores of each transaction, which helps to trust and be obedient of the financial regulations. Experiments were carried out on multi-institutional data of credit cards (2.3M + transactions) using a heterogeneous bank simulated testbed. The test data indicates the 4.8 increase in the recall of the original BigBird counterparts, and the 3.2 reduction in the false positives at the baseline Random Forest models on addition of sub-300ms to the real-time prediction. The SHAP-based explanations indicated important temporal-spending deviations that align with the knowledge of the researcher of the fraud case. These findings verify that the suggested system can provide the right combination of accuracy and low latency and be interpretable, therefore, be applied to distributed financial systems. Physical sciences/Mathematics and computing Physical sciences/Physics Federated Learning BiLSTM–CNN Credit Card Fraud Detection GAN Oversampling Explainable AI SHAP LIME Imbalanced Data Real-Time Inference Privacy-Preserving AI Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7587282","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":518324538,"identity":"7ce1418d-cc51-4493-a0a9-9bf4fc8b1619","order_by":0,"name":"Sundaravadivel P","email":"","orcid":"","institution":"Saveetha Engineering Colege","correspondingAuthor":false,"prefix":"","firstName":"Sundaravadivel","middleName":"","lastName":"P","suffix":""},{"id":518324539,"identity":"d0b529b0-1478-4e66-9239-d466d71bf7df","order_by":1,"name":"Augustian Isaac 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The existing ensemble learning systems (Random Forest\u0026thinsp;+\u0026thinsp;SMOTE) are extended into the new Federated Deep Learning (FDL) and into the Explainable AI (XAI) leading to the fraud detection in real-time. The proposed framework is a combination of the BiLSTM-CNN hybrid model to track temporal as well as spatial patterns within the transactions and, in the meantime, preserve the data locality by means of a federated aggregation. Extreme class imbalance is one of the challenging problems in machine learning application and, hence an oversampling process with the help of the Generative Adversarial Network (GAN) is proposed to learn the minority distribution of frauds better in comparison to a conventional SMOTE. Moreover, layers of explanability like SHAP and LIME are implemented to have explainable risk scores of each transaction, which helps to trust and be obedient of the financial regulations. 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