ADAPTIVE STREAMING INTELLIGENCE FOR REAL-TIME FINANCIAL FRAUD DETECTION: A KAFKA-ORIENTED MACHINE LEARNING FRAMEWORK
Keywords:
Real-time fraud detection, streaming analytics, adaptive machine learningAbstract
Financial transaction fraud has evolved into a dynamic, adversarial problem that demands detection systems capable of operating continuously at event velocity while remaining adaptive, explainable, and auditable. This article constructs a comprehensive theoretical and design framework—Adaptive Streaming Intelligence (ASI)—for real-time financial fraud detection that situates machine learning models within robust streaming infrastructures (with Apache Kafka as the canonical substrate), integrates hybrid model families (supervised classifiers, anomaly detectors, graph analytics, and generative adversarial techniques), and embeds alarm-verification and human-in-the-loop governance to manage false positives and regulatory obligations. Drawing on prior empirical and architectural studies (Rajeshwari & Babu, 2016; Hanae et al., 2023; Manoharan et al., 2024), as well as contemporary reviews of AI approaches in fraud prevention (Bello et al., 2023; Rahman et al., 2024), the ASI framework prescribes layered processing: ultralow-latency fast paths for authorization decisions, contextual mid-path scoring for refinement, deferred deep analysis for network-level patterns, and adaptive model lifecycle processes for continuous learning. The framework details feature engineering patterns for streaming contexts, techniques for handling class imbalance and concept drift, ensemble and online adaptation strategies, and operational principles for observability, privacy, and forensic traceability. The article concludes with a prioritized empirical agenda—sandbox pilots, adversarial stress tests, and cross-institutional trials—designed to validate performance expectations and to map governance requirements across jurisdictions. This synthesis aims to offer practical theoretical guidance for both researchers and practitioners striving to make real-time fraud detection resilient, scalable, and ethically accountable.
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