Adaptive resource allocation for real-time processing during payment volume spikes: ML-driven infrastructure orchestration

This paper presents a comprehensive framework for adaptive resource allocation in banking payment processing systems during high-volume transaction periods. We demonstrate how machine learning techniques can optimize infrastructure orchestration to maintain performance standards while minimizing operational costs. Our experimental implementation across three financial institutions shows a 37% reduction in processing latency and a 24% decrease in infrastructure costs during peak periods compared to static provisioning methods. The research addresses critical challenges in modern banking systems where traditional fixed-capacity approaches fail to efficiently handle increasingly unpredictable transaction volume spikes. We provide detailed architectural components, ML model evaluations, and integration pathways for financial institutions seeking to implement similar solutions.