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.

Real-time fraud detection, streaming analytics, deep learning

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.