AI-Powered Test Automation Frameworks for Continuous Delivery in Banking Software Ecosystems

Abstract

In the rapidly evolving banking software landscape, the demand for robust, scalable, and intelligent testing mechanisms has surged, primarily due to continuous delivery (CD) pipelines and regulatory requirements. This paper explores the integration of AI-powered test automation frameworks that enhance test coverage, reduce regression cycles, and ensure reliability in high-stakes financial environments. By incorporating machine learning, anomaly detection, and intelligent test case generation, these frameworks support faster deployment cycles without compromising software quality. The focus is on evaluating architectural patterns, performance metrics, and real-world deployment strategies in banking ecosystems.