Citation:

Srikanth Chakravarthy Vankayala, 2023. "Reinforcement Learning–Driven Cognitive Testing for Scalable and Resilient Financial Systems  3(4): 209-217.

Abstract:

High-throughput financial systems require continuous validation through cognitive and adaptive testing frameworks to ensure performance, resilience, and correctness under dynamic workloads, particularly in environments characterized by non-stationary data, bursty transaction patterns, and strict latency constraints. Traditional rule-based testing approaches fail to scale with the complexity and velocity of modern financial infrastructures, as they rely on static heuristics, lack contextual awareness, and are unable to respond effectively to evolving system states or emerging failure modes. This paper proposes a reinforcement learning (RL)-driven framework for cognitive test optimization, where testing strategies are dynamically adapted based on real-time system feedback, historical performance data, and probabilistic risk signals. By modeling testing pipelines as Markov Decision Processes (MDPs) and leveraging actor-critic architectures, the proposed approach enables intelligent prioritization of test cases, efficient allocation of computational and network resources, and continuous policy refinement through reward-driven learning. Furthermore, the framework incorporates exploration–exploitation balancing to uncover previously unseen edge cases while maintaining operational efficiency, making it particularly suitable for high-frequency trading platforms and real-time risk engines.

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Keywords:

Reinforcement Learning, Cognitive Testing, Financial Systems, High-Throughput Systems, Test Optimization, Actor-Critic Models, Adaptive Systems, Markov Decision Process, AI in Finance.