Computational Intelligence Model for Accelerating Payment Processing and Financial Resource Allocation in Logistics Networks

Abstract

The rapid expansion of global logistics networks has increased the complexity of payment processing, financial coordination, and resource allocation among interconnected stakeholders. Traditional financial workflows often face limitations due to delayed transactions, fragmented information systems, inefficient decision-making mechanisms, and difficulties in managing dynamic supply chain environments. This research presents a computational intelligence model designed to accelerate payment processing and optimize financial resource allocation within logistics networks by integrating intelligent decision mechanisms, modular software architectures, and adaptive learning approaches.
The proposed model combines computational intelligence techniques with microservices-based infrastructure and event-driven communication frameworks to enable scalable, responsive, and automated financial operations. The methodology incorporates intelligent payment prediction, reinforcement-based optimization, distributed service management, and real-time financial resource balancing. The model architecture is designed around independent service components that support payment verification, transaction routing, risk evaluation, and liquidity optimization. Microservices principles enable flexible integration between logistics platforms, financial institutions, and enterprise resource management systems, while computational intelligence provides adaptive capabilities for handling uncertainty and changing operational conditions.
The research builds upon existing studies related to microservices architecture, intelligent financial optimization, and automated decision-making. Previous work on hybrid reinforcement and deep learning approaches demonstrates the potential of artificial intelligence models in reducing payment delays and improving supply chain finance performance (D. SinghJatav et al., 2025). Similarly, microservices-based approaches provide structural advantages for developing distributed financial applications with improved scalability and maintainability (Waseem and Liang, 2017).