A Control-Theoretic Approach to Dynamic Payment Routing for Success Rate Optimization

This paper introduces a control-theoretic framework for dynamic payment routing, implemented within JUSPAY's Payment Orchestrator to maximize transaction success rate. The routing system is modeled as a closed-loop feedback controller continuously sensing gateway [3] performance, computing corrective actions, and dynamically routes transactions across gateway to ensure operational resilience. The system leverages concepts from control theory, reinforcement learning, and multi-armed bandit optimization to achieve both short-term responsiveness and long-term stability. Rather than relying on explicit PID regulation, the framework applies generalized feedback-based adaptation, ensuring that corrective actions remain proportional to observed performance deviations and the computed gateway score gradually converges toward the success rate [2]. This hybrid approach unifies control theory and adaptive decision systems, enabling self-regulating transaction routing that dampens instability, and improves reliability. Live production results show an improvement of up to 1.15% in success rate over traditional rule-based routing, demonstrating the effectiveness of feedback-based control in payment systems.