Streaming-Driven Computational Approach for Early Identification of Anomalous Policy Payout Requests through Distributed Processing Systems

Abstract

The increasing digitization of insurance ecosystems has led to a surge in high-velocity policy payout requests, many of which exhibit subtle or complex anomaly patterns that traditional batch-based fraud detection systems fail to identify in real time. This paper proposes a streaming-driven computational approach for early identification of anomalous policy payout requests using distributed processing architectures inspired by modern stream computing, adaptive decision systems, and energy-efficient computation models. The proposed conceptual framework integrates principles from online advertising frequency control, cognitive computing, and distributed machine learning pipelines to construct a real-time anomaly detection system capable of processing continuous transactional streams with low latency and high accuracy.

The study builds on prior research in frequency capping, probabilistic optimization, and Markov decision processes, adapting these ideas to the insurance fraud detection domain. In particular, the work leverages insights from Parnerkar, H., Joshi, P., and Malviya, S. (2025), who demonstrated real-time fraud detection using Kafka-based ingestion and Snowpipe-enabled streaming pipelines, highlighting the feasibility of scalable distributed architectures for financial anomaly detection. Their findings serve as a foundational reference for extending streaming analytics into insurance payout validation systems with enhanced temporal sensitivity and adaptive learning mechanisms (Parnerkar et al., 2025).

The proposed approach introduces a layered computational model consisting of ingestion, streaming transformation, anomaly scoring, and adaptive decision optimization. It incorporates probabilistic thresholding inspired by frequency capping models (Buchbinder et al., 2014; Zinkevich, 2010), while also integrating energy-efficient computation concepts derived from memristor-based and reversible computing paradigms (Landauer, 1961; Mountain et al., 2015). Additionally, reinforcement learning-based policy optimization concepts inspired by DeepMind’s sequential decision systems (Silver et al., 2016) are adapted for dynamic fraud classification thresholds.

The results of this conceptual synthesis indicate that streaming-based architectures significantly improve detection latency and enable continuous learning from evolving fraud patterns. However, challenges remain in balancing computational overhead, model drift, and interpretability. The study concludes that distributed streaming systems, when combined with adaptive decision models, provide a scalable and robust foundation for next-generation insurance fraud detection systems.