ARTIFICIAL INTELLIGENCE ADOPTION IN RETAIL BANKING: EFFECTS ON CUSTOMER SATISFACTION, CREDIT SCORING ACCURACY, AND OPERATIONAL EFFICIENCY”

Abstract

The rapid advancement of artificial intelligence (AI) has significantly transformed the retail banking sector by enhancing service delivery, risk assessment, and internal processes. This study examines the impact of AI adoption in retail banking on three key dimensions: customer satisfaction, credit scoring accuracy, and operational efficiency. Using a mixed-methods approach that combines quantitative analysis of banking performance indicators with qualitative insights from customer surveys and expert interviews, the research evaluates how AI-driven tools such as chatbots, machine learning–based credit scoring models, and robotic process automation contribute to improved banking outcomes. The findings indicate that AI adoption positively influences customer satisfaction through personalized and faster services, increases credit scoring accuracy by reducing human bias and error, and enhances operational efficiency by lowering costs and processing time. The study provides practical implications for bank managers and policymakers seeking to promote digital transformation in the financial sector.