Enhancing Quality Assurance Processes in BFSI SystemsUsing AI-Based Predictive Analytics
Keywords:
AI in BFSI, Predictive Analytics, Quality Assurance, Machine Learning, Process Optimization, Software Testing, Risk ManagementAbstract
The Banking, Financial Services, and Insurance (BFSI) sector is increasingly adopting artificial intelligence (AI) to streamline operations, reduce costs, and enhance customer experience. One area gaining traction is the application of AI-based predictive analytics in quality assurance (QA) processes. In this paper, we explore the integration of predictive analytics into QA workflows to identify defects earlier, improve compliance, and optimize testing efforts. The study evaluates current AI technologies and their impact on QA effectiveness across BFSI platforms, drawing on case studies, industry benchmarks, and historical data. The findings suggest that predictive models significantly improve fault detection rates, reduce time-to-resolution, and support proactive risk management. This paper contributes to a growing body of work on AI-driven process optimization in BFSI, providing a roadmap for future research and implementation.
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