Hybrid Streaming Intelligence For Real-Time Transactional Fraud Detection: A Kafka-Centric Machine Learning Framework

Abstract

Financial transaction fraud in contemporary digital ecosystems is a fast-moving adversarial problem that requires detection systems to be not only accurate but immediate, adaptive, and auditable. This article develops a comprehensive theoretical and design-oriented framework for hybrid streaming intelligence that unites stateful event streaming (with Apache Kafka as the canonical substrate), multiple machine learning paradigms (supervised classification, anomaly detection, graph-based link analysis, and ensemble voting), and alarm-verification mechanisms including text analytics for enriched context. Drawing upon prior technical and academic work on streaming architectures, ML workflow automation, hybrid alarm verification, transaction synthesis, and practical implementations of fraud scoring, the framework articulates how Kafka-centric event topologies can host low-latency triage, mid-path contextual scoring, and deferred deep analysis while preserving system stability and supporting continuous model adaptation (Dunning & Friedman, 2016; Crettaz & Dean, 2019; Sima et al., 2018). The article provides a detailed methodological rationale for mapping ML models into stream processors and microservices, explains feature engineering patterns suitable for streaming contexts, addresses class imbalance and concept drift, and presents a layered alarm-verification design that reduces false positives and operational costs (Singh, 2019; Sahai & Gursoy, 2019). It further examines how graph analytics, generative models, and ensemble adaptive online learning contribute to network-level detection and adversarial resilience (Molloy et al., 2016; Goodfellow et al., 2014; Roshan & Zafar, 2024). The discussion evaluates trade-offs in latency, interpretability, and governance, and proposes an empirical research agenda including sandbox pilots, red-team adversarial testing, and standards for forensic logging. The contribution is a publication-ready synthesis intended to guide both researchers and practitioners seeking to deploy robust, production-grade real-time fraud detection in high-throughput FinTech environments.