AI-Augmented Test Automation Frameworks for Banking Applications

The need to have fast and scalable software systems that are secure, compliant, and robust is not only driven by the increased rate of digitalization that has occurred in the banking sector, but also by the growing concern around security.Weaker financial systems that are more susceptible to regulations are often not automated under the usual testing procedures.This paper is interlinked with the possibilities of improving test automation systems through the introduction of Artificial Intelligence (AI) within the banking sector.It emphasizes that the adoption of AI technologies-such as machine learning, natural language processing, and predictive analytics-will create opportunities to design intelligent test cases, enable self-healing systems, and ensure verification of strict compliance.The AI-driven designs, which include test pipelines as part of agile practices and the DevOps culture in financial institutions, are discussed.It also analyses the potential of AI in modernizing legacy systems, making them fault-tolerant, simplifying Anti-Money Laundering (AML) processes, and ensuring regulatory compliance.The discussion of new trends and the practical application of AI-enhanced test automation in the high-stakes banking environment represent the disruptive prospects and challenges in this area and form the foundation of the paper.