Adaptive Financial Infrastructure: A DevOps–Machine Learning Framework for Predictive Resource and Operational Optimization

Financial systems operate under strict requirements for availability, low latency, resilience, and regulatory compliance, yet infrastructure management in these environments remains largely reactive. This paper addresses that limitation by proposing a DevOps–Machine Learning framework for predictive resource and operational optimization in adaptive financial infrastructure. The study follows an artefact-oriented approach inspired by Design Science Research: the framework is defined conceptually, instantiated as a containerized Proof of Concept, and evaluated through controlled benchmarking. The proposed architecture integrates observability, workload forecasting, decision support, and automated actuation to support proactive scaling and more adaptive operational control in cloud-based financial environments. The experimental setup uses synthetic financial-like workloads with cyclical demand, stochastic variation, and sudden spikes to compare conventional reactive scaling with forecast-enhanced strategies. The results indicate that embedding predictive intelligence into infrastructure operations improves the ability to anticipate workload changes and offers a more structured basis for balancing responsiveness, operational control, and resource efficiency than purely threshold-based mechanisms. The study concludes that predictive resource and operational optimization in financial systems should not be treated as an isolated autoscaling problem, but as part of a broader DevOps–Machine Learning architecture for adaptive financial infrastructure.