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International Journal of Advanced Artificial Intelligence Research

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Integrated Real-Time Fraud Detection and Response: A Streaming Analytics Framework for Financial Transaction Security

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Dr. Leila K. Moreno 1 ,
4 School of Information Systems, University of Sydney, Australia

Abstract

This article develops a comprehensive and practically oriented research contribution that synthesizes streaming analytics, deep learning, and pragmatic systems engineering to create an integrated framework for real-time detection and response to financial transaction fraud. The work builds on contemporary technical reports and peer-reviewed research on streaming platforms (Kafka, Spark, Flink), real-time fraud detection models, anti-money-laundering (AML) machine learning, and operational best practices in financial services (Rajeshwari & Babu, 2016; Abakarim et al., 2018; Nicholls et al., 2021; Saxena & Gupta, 2017). The proposed Integrated Streaming Fraud Response Framework (ISFRF) is defined by four tightly coupled layers—Event Ingestion & Stream Processing, Low-Latency Triage & Authorization, Contextual Enrichment & Deep Analysis, and Forensic Evidence & Governance—and is accompanied by a suite of engineering patterns for feature computation in streaming contexts, drift-adaptive learning, adversarial resilience, privacy-preserving forensic commitments, and human-in-the-loop adjudication. Methodologically, the article employs a structured conceptual synthesis of the literature and derives implementation prescriptions, evaluation metrics adapted to streaming contexts, and a multi-stage empirical agenda including realistic sandbox pilots, adversarial red-team testing, and cross-institutional studies for network-level detection. The analysis emphasizes trade-offs—latency vs. complexity, explainability vs. predictive power, and privacy vs. auditability—and offers concrete design decisions (such as hybrid on-chain/off-chain commitments for audit integrity and tiered model pipelines) to reconcile these tensions. Finally, the work identifies critical gaps in extant knowledge—particularly the scarcity of longitudinal, production-scale evaluations and adversarial field studies—and proposes a prioritized research roadmap for practitioners and scholars engaged in building resilient, trustworthy transaction monitoring systems.

Keywords

Real-time fraud detection, streaming analytics, deep learning

References

Federal Trade Commission. (2024). Investment Scams 2024. Retrieved from https://www.ftc.gov
Axios. (2024). Impostor Scam Trends 2024. Retrieved from https://www.axios.com
Axios. (2024). Fraud Losses by State in 2024. Retrieved from https://www.axios.com
News. (2024). Millennials and Credit Card Fraud in Australia 2024. Retrieved from https://www.news.com.au
Xu, J., Yang, T., Zhuang, S., Li, H., & Lu, W. (2024). AI-Based Financial Transaction Monitoring and Fraud Prevention with Behaviour Prediction. Applied and Computational Engineering, 67, 76-82.
Gartner Hype. (2024). Gartner Hype Cycle for Artificial Intelligence: A Comprehensive Guide. Retrieved from https://aicoach.co.za/2024-hype-cycle-for-artificial-intelligence/
Hebbar, K. S. (2025). AI-DRIVEN REAL-TIME FRAUD DETECTION USING KAFKA STREAMS IN FINTECH. International Journal of Applied Mathematics, 38(6s), 770-782.
Glassbox. (2023). Customer acquisition in banking: 10 proven strategies you should implement right now. Retrieved from https://www.glassbox.com/blog/customeracquisition-in-banking/
Kaushik Sathupadi et al. (2025). BankNet: Real-Time Big Data Analytics for Secure Internet Banking. Big Data and Cognitive Computing.
Kai Waehner. (2023). The State of Data Streaming for Financial Services. Retrieved from https://www.kai-waehner.de/blog/2023/04/04/the-state-of-data-streaming-for-financial-services-in-2023/
Noussair Fikri et al. (2019). An Adaptive and Real-Time Based Architecture for Financial Data Integration. Journal of Big Data, 6(1).
Tundis, A., Nemalikanti, S., & MĂŒhlhĂ€user, M. (2021). Fighting organized crime by automatically detecting money laundering-related financial transactions. Proceedings of the 16th International Conference on Availability, Reliability and Security.
Jullum, M., Lþland, A., Huseby, R. B., Ånonsen, G., & Lorentzen, J. (2020). Detecting money laundering transactions with machine learning. Journal of Money Laundering Control, 23(1), 173–186.
Zhang, Y., & Trubey, P. (2019). Machine learning and sampling scheme: an empirical study of money laundering detection. Computational Economics, 54, 1043–1063.
Labanca, D., Primerano, L., Markland-Montgomery, M., Polino, M., Carminati, M., & Zanero, S. (2022). Amaretto: an active learning framework for money laundering detection. IEEE Access, 10, 41720–41739.
García-Bedoya, O., Granados, O., & Cardozo Burgos, J. (2021). AI against money laundering networks: the Colombian case. Journal of Money Laundering Control, 24(1), 49–62.
Segovia-Vargas, M.-J., et al. (2021). Money laundering and terrorism financing detection using neural networks and an abnormality indicator. Expert Systems with Applications, 169, 114470.
Guevara, J., GarcĂ­a-Bedoya, O., & Granados, O. (2020). Machine learning methodologies against money laundering in non-banking correspondents. In Applied Informatics: Third International Conference, ICAI 2020.
Larik, A. S., & Haider, S. (2011). Clustering based anomalous transaction reporting. Procedia Computer Science, 3, 606–610.
Alexandre, C., & Balsa, J. (2015). Client profiling for an anti-money laundering system. arXiv:1510.00878.
Sahin, Y., & Duman, E. (2011). Detecting credit card fraud by decision trees and support vector machines.
Tang, J., & Yin, J. (2005). Developing an intelligent data discriminating system of anti-money laundering based on SVM.
Cardoso, M., Saleiro, P., & Bizarro, P. (2022). LaundroGraph: self-supervised graph representation learning for anti-money laundering.
Khan, W., & Haroon, M. (2022). An efficient framework for anomaly detection in attributed social networks. International Journal of Information Technology, 14(6), 3069–3076.
Kashika, P., & Venkatapur, R. B. (2022). Automatic tracking of objects using improvised YOLOv3 algorithm and alarm human activities in case of anomalies. International Journal of Information Technology, 14(6), 2885–2891.
Iliyasu, A. S., & Deng, H. (2022). N-gan: a novel anomaly-based network intrusion detection with generative adversarial networks. International Journal of Information Technology, 14(7), 3365–3375.
GĂłmez, J. A., ArĂ©valo, J., Paredes, R., & Nin, J. (2018). End-to-end neural network architecture for fraud scoring in card payments. Pattern Recognition Letters, 105, 175–181.
Remmide, M. A., Boumahdi, F., Ilhem, B., & Boustia, N. (2024). A privacy-preserving approach for detecting smishing attacks using federated deep learning. International Journal of Information Technology.
Abbassi, H., Abdellah, B., Mendili, S., & Youssef, G. (2023). End-to-end real-time architecture for fraud detection in online digital transactions. International Journal of Advanced Computer Science and Applications.
Alkhalili, M., Qutqut, M. H., & Almasalha, F. (2021). Investigation of applying machine learning for watch-list filtering in anti-money laundering. IEEE Access.
The Business Research Company. (Jan. 2025). Financial Services Market Definition. The Business Research Company Insight.

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