Abstract

In an era dominated by digital transactions and real-time decision-making, traditional fraud detection systems have become inadequate due to their reliance on delayed, batch-based processing. This research presents an event-driven architecture for real-time fraud detection, leveraging Apache Kafka for high-throughput data ingestion, ksqlDB for rule-based stream querying, and Apache Flink for complex event processing and machine learning inference. The system ingests transaction, login, and geolocation data streams, applies immediate filters, and performs stateful anomaly detection to identify suspicious behaviors such as velocity violations and improbable access patterns. A fully containerized implementation validates the architecture’s performance under simulated load conditions, achieving a true positive rate of 94.2% and sub-second latency. The hybrid approach unifies rule-based and ML-enhanced detection, offering low false positives and high adaptability. This work demonstrates how modern stream processing technologies can transform fraud detection from a reactive, offline task into a proactive, real-time analytics pipeline embedded within the data infrastructure. The architecture is modular, scalable, and production-ready, making it suitable for deployment in financial and e-commerce ecosystems.

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