Stanford Database Library of American Journal of Applied Science and Technology

Vol. 5 No. 10 (2025)
Articles

Streaming Intelligence For Real-Time Fraud Detection: A Practical And Theoretical Framework Using Online Learning, Anomaly Detection, And Stream Processing

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  • Dr. Kavita R. Iyer
Dr. Kavita R. Iyer
Department of Computer Science and Engineering, Indian Institute of Technology Madras, India

Published 2025-10-31

Keywords

  • Real-time fraud detection,
  • streaming analytics,
  • online learning

How to Cite

Dr. Kavita R. Iyer. (2025). Streaming Intelligence For Real-Time Fraud Detection: A Practical And Theoretical Framework Using Online Learning, Anomaly Detection, And Stream Processing. Stanford Database Library of American Journal of Applied Science and Technology, 5(10), 317–323. Retrieved from https://oscarpubhouse.com/index.php/sdlajast/article/view/57

Copyright (c) 2025 Dr. Kavita R. Iyer

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Financial fraud has become a dynamic, high-velocity adversarial problem driven by the global scale of digital payments, card-not-present commerce, and instantaneous settlement rails. Rapid detection requires systems that combine low-latency stream processing, adaptive machine intelligence, and robust operational governance. This article develops a unified, publication-ready framework for streaming fraud intelligence that synthesizes architectural patterns (Kafka-style ingestion and materialized state), online machine learning methods (incremental learners, adaptive windowing), anomaly detection approaches (Isolation Forests, LOF), ensemble and gradient-boosted tree methods (XGBoost, LightGBM), and graph-based network detection techniques. Building on seminal and contemporary research (Rajeshwari & Babu, 2016; Carcillo et al., 2018; Bifet & Gavalda, 2007) and practitioner resources (Redis Inc., 2023; Tinybird Blog, 2023), the framework prescribes a layered pipeline: ultralow-latency fast path for authorization decisions, contextual mid-path scoring for refined risk, deferred deep analysis for network and laundering detection, and alarm-verification with human-in-the-loop adjudication. We elaborate feature-engineering patterns suitable for streaming environments (bounded sliding windows, exponential decay aggregates, probabilistic sketches), detail drift detection and mitigation strategies (adaptive windows, online weight adaptation, active learning), and discuss trade-offs among latency, accuracy, explainability, and regulatory accountability. Finally, we propose a prioritized empirical validation program—shadow deployments, synthetic adversarial injections, and federated cross-institution pilots—and operational controls for auditability and privacy. This synthesis provides researchers and practitioners with a conceptual and operational blueprint to design resilient, explainable, and deployable real-time fraud detection systems.

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