AI-Powered Test Automation for Financial Cloud Applications Using LLMs and Serverless Computing

This article proposes a conceptual framework for AI-powered testing of financial cloud applications by integrating Large Language Models (LLMs) with serverless computing architecture. The framework addresses critical challenges in financial software testing, including regulatory compliance, security validation, and performance scalability. Through theoretical analysis, the design incorporates sophisticated prompt engineering techniques and automated compliance monitoring systems to achieve high compliance validation accuracy while potentially reducing monitoring costs. The architecture suggests superior performance capabilities, potentially processing concurrent test executions efficiently while reducing operational costs compared to traditional testing infrastructures. The framework design anticipates improvements in test generation efficiency, regulatory compliance coverage, and overall testing costs while maintaining robust security standards through enhanced vulnerability detection mechanisms. The theoretical integration of LLMs with serverless computing shows promise in addressing the complex challenges of financial software testing, offering a potential pathway to a scalable, cost-effective solution that could meet the stringent requirements of modern financial cloud applications while ensuring regulatory compliance and security.