AI-driven retail banking ecosystems: a framework for improving customer experience, risk management, and operational efficiency

The current study has been conducted to propose and validate the artificial intelligence (AI)-driven banking ecosystem model for understanding how AI improves customer experience, risk management, and operational efficiency within the context of the banking industry. Specifically, the study aims to overcome the lack of empirical studies providing holistic evidence on how AI works in emerging regional banking markets by analysing the influence of AI awareness, AI application, and AI effectiveness on banking performance. The study utilises quantitative and explanatory research designs and data collection procedures. In total, 480 participants, including customers, employees, and branch managers of scheduled commercial banks have been surveyed. The AI-driven banking ecosystem has been defined using the constructs of AI awareness, AI application, and AI effectiveness. Descriptive statistics, reliability and validity testing, CFA, and SEM have been used for data analysis. The findings show that the AI-driven banking ecosystem positively influences all dimensions of banking performance. In addition, risk management (β = 0.751, p < 0.001), operational efficiency (β = 0.704, p < 0.001), and customer experience (β = 0.582, p < 0.01) had the highest impact on performance in the banking industry. The structural model presented satisfactory goodness-of-fit indicators, which confirmed the conceptual framework of the research and all the hypotheses of the study. In addition, the research suggests the development of an integrated AI driven banking ecosystem framework that takes into account technological, organisational, and strategic aspects of AI.