Generative Artificial Intelligence and Large Language Models on Retail Banking Effectiveness: Evidence from Hong Kong

Abstract

This study examines the impact of Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) on retail banking effectiveness in Hong Kong. Drawing on an integrated theoretical framework combining the Technology Acceptance Model (TAM), Task-Technology Fit (TTF), and Resource-Based View (RBV), the research adopts a mixed-methods approach using a convergent parallel design. Quantitative data were collected from 100 retail banking users using structured questionnaires, while qualitative insights were obtained from semi-structured interviews with 50 banking employees. The quantitative findings reveal that both GenAI usage and LLM integration have significant positive effects on retail banking effectiveness, with LLM integration emerging as the stronger predictor. The model explains a substantial proportion of variance in performance outcomes, particularly in operational efficiency, customer satisfaction, and decision accuracy. Complementary qualitative findings indicate that AI technologies enhance banking operations through automation, improved customer service, and data-driven decision-making. However, key challenges such as system integration, implementation costs, and concerns regarding transparency and reliability persist. The study contributes to the literature by providing empirical evidence from a major financial hub and demonstrating how technological, organisational, and task-related factors jointly influence AI-driven performance. The findings offer practical implications for financial institutions seeking to leverage AI for competitive advantage while addressing ethical and operational challenges.

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