“Impact of AI-Based Decision Support Systems on Operational Efficiency of Public Sector Banks”

Abstract: Artificial Intelligence (AI) is redefining how public sector banks operate in India by introducing smart Decision Support Systems (DSS) that enhance efficiency and accuracy in decision-making. This research paper investigates how AI-based DSS contributes to operational efficiency in public sector banks, especially in core banking operations, risk management, customer service, and process automation. The study context is grounded in the Indian banking landscape, where digital transformation has accelerated due to competitive pressures and customer expectations. The research synthesizes primary concepts of AI, DSS frameworks, and operational efficiency metrics to understand the depth of AI adoption and its practical effects on public sector banks. Review of recent Indian academic research shows significant positive correlations between AI integration and improvements in service delivery, workflows, risk mitigation, and back-office task optimization. However, implementation challenges such as data quality, regulatory constraints, technical know-how, and ethical considerations persist. By employing a mixed-method methodology combining descriptive analysis and inferential evidence from secondary sources, this paper discusses both qualitative and quantitative implications of AI-driven DSS. It concludes that while AI-enabled systems can considerably reduce processing time, errors, and operational costs, the banks need supportive infrastructure, skilled workforce training, and robust governance frameworks to fully leverage these systems. The study’s findings provide actionable insights for policymakers, banking executives, and technology strategists to enhance operational efficiency and guide future research on advanced AI integration in public sector banking.

Keywords: Artificial Intelligence, Decision Support Systems, Public Sector Banks, Operational Efficiency, India