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Global Multidisciplinary Journal

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Real-Time Credit Card Fraud Detection With Streaming Analytics: A Convergent Framework Using Kafka, Deep Learning, And Hybrid Provenance

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Dr. Anika Moreau 1 ,
4 Department of Computer Science, University of Melbourne, Australia

Abstract

This article develops a comprehensive, publication-ready synthesis and original framework for near real-time credit card fraud detection grounded in streaming analytics, deep learning, and pragmatic system design. Drawing from empirical and methodological literature on real-time fraud detection, streaming platforms (Kafka, Spark, Flink), deep learning architectures, large-scale anomaly detection, and operational constraints in financial systems, the paper articulates a resilient architectural pattern that balances latency, detection accuracy, explainability, and data governance (Abakarim et al., 2018; Rajeshwari & Babu, 2016; Martín Hernández, 2015; Hebbar, 2025). The proposed Convergent Streaming Detection Framework emphasizes a tiered detection pipeline: ultrafast rule-based triage in the streaming path, lightweight explainable models for immediate scoring, and contextual deep models (including sequence and graph-based learners) operating on enriched windows for elevated scrutiny (Nicholls et al., 2021; Zhou et al., 2019). Practical considerations include feature engineering for streaming contexts, approaches to class imbalance and concept drift, strategies for low-latency model serving, and hybrid provenance and logging to preserve forensic trails without violating privacy or incurring prohibitive storage and throughput costs (Saxena & Gupta, 2017; Nguyen et al., 2020). The article also details rigorous evaluation metrics appropriate to streaming fraud contexts, an experimental design for realistic pilot deployments, adversarial threat modeling, and a multi-year research agenda emphasizing red-team testing and socio-technical evaluation. The synthesis stresses that engineering trade-offs—between latency and model complexity, explainability and predictive performance, and on-chain/off-chain evidence storage—must be made transparently and governed by regulatory and user-centric considerations (The Business Research Company, 2025; Udeh et al., 2024). The contribution is a practically actionable blueprint for researchers and practitioners seeking to deploy deep-learning-driven fraud detection in production-grade streaming environments.

Keywords

Real-time fraud detection, streaming analytics, deep learning

References

📄 Rajeshwari, U., and B. Sathish Babu. Real-time credit card fraud detection using streaming analytics. 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT). IEEE, 2016.
📄 Martín Hernández, Sergi. Near real time fraud detection with Apache Spark. 2015.
📄 Jayanthi, D., G. Sumathi, and Sriperumbudur Sriperumbudur. A framework for real-time streaming analytics using a machine learning approach. Proceedings of national conference on communication and informatics. 2016.
📄 Zhou, Hangjun, et al. A scalable approach for fraud detection in online e-commerce transactions with big data analytics. Computers, Materials & Continua 60.1 (2019): 179-192.
📄 Saxena, Shilpi, and Saurabh Gupta. Practical real-time data processing and analytics: distributed computing and event processing using Apache Spark, Flink, Storm, and Kafka. Packt Publishing Ltd, 2017.
📄 Nicholls, J., Kuppa, A., & Le-Khac, N.-A. Financial cybercrime: a comprehensive survey of deep learning approaches to tackle the evolving financial crime landscape. IEEE Access, 2021.
📄 Chen, Z., Van Khoa, L. D., Teoh, E. N., Nazir, A., Karuppiah, E. K., & Lam, K. S. Machine learning techniques for anti-money laundering (AML) solutions in suspicious transaction detection: a review. Knowledge and Information Systems, 2018.
📄 Jensen, R., & Iosifidis, A. Fighting money laundering with statistics and machine learning. IEEE Access, 2023.
📄 Demetis, D. S. Fighting money laundering with technology: a case study of bank x in the UK. Decision Support Systems, 2018.
📄 Chen, Z., Soliman, W. M., Nazir, A., & Shorfuzzaman, M. Variational autoencoders and Wasserstein generative adversarial networks for improving the anti-money laundering process. IEEE Access, 2021.
📄 Abbassi, H., Abdellah, B., Mendili, S., & Youssef, G. End-to-end real-time architecture for fraud detection in online digital transactions. International Journal of Advanced Computer Science and Applications, 2023.
📄 Alkhalili, M., Qutqut, M. H., & Almasalha, F. Investigation of applying machine learning for watch-list filtering in anti-money laundering. IEEE Access, 2021.
📄 The Business Research Company. Financial Services Market Definition. The Business Research Company Insight. Jan. 2025.
📄 Stéphane Derosiaux. The Rise of Data Streaming and the Evolution of Data at Rest: 2018-2024. Medium. 5 Feb. 2025.
📄 Ezekiel Onyekachukwu Udeh et al. The role of big data in detecting and preventing financial fraud in digital transactions. World Journal of Advanced Research and Reviews. 24 May 2024.
📄 Hivemind Technologies. Apache Kafka in the Financial Sector: Real-Time Data Processing for Banking Operations. LinkedIn Pulse. 29 Oct. 2024.
📄 Jim Marous. Improving the Customer Experience in Banking. Digital Banking Report. Feb. 2017.
📄 Seshika Fernando. Real-Time Analytics in Banking and Finance: Use Cases. WSO2 Whitepaper. April 2017.
📄 Hebbar, K. S. AI-DRIVEN REAL-TIME FRAUD DETECTION USING KAFKA STREAMS IN FINTECH. International Journal of Applied Mathematics, 38(6s), 770-782. 2025.
📄 Wang, J., & Liu, Q. Advancements in AI and Machine Learning for Financial Fraud Detection. International Journal of Data Science, 2023.
📄 Baah, S. S., Adu-Twum, H. T., Adjei, S. O., Ampadu, G., Martins, A. O., & Fonkem, B. Leveraging big data analytics to combat emerging financial fraud schemes in the USA: A literature review and practical implications. World Journal of Advanced Research and Reviews, 2024.
📄 Uddin, N. Significance of Live Streaming in Shaping Business: A Critical Review and Analytical Study. Social Networking, 2024.
📄 Federal Trade Commission. Consumer Sentinel Network Data Book. 2024.
📄 The Motley Fool. Report on Identity Theft. 2024.
📄 The Motley Fool. Credit Card Fraud Statistics. 2024.
📄 Wang and Liu. Advancements in AI and Machine Learning for Financial Fraud Detection. 2023.
📄 Baah et al. Leveraging big data analytics to combat emerging financial fraud schemes. 2024.
📄 Uddin, N. Significance of Live Streaming in Shaping Business. 2024.

How to Cite

Dr. Anika Moreau. (2025). Real-Time Credit Card Fraud Detection With Streaming Analytics: A Convergent Framework Using Kafka, Deep Learning, And Hybrid Provenance. Global Multidisciplinary Journal, 4(11), 111-119. https://www.grpublishing.org/journals/index.php/gmj/article/view/233

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