Artificial intelligence is changing the banking sector by automating customer service, customised recommendations for all types of financial products, risk assessment of credit and other areas, early warning of fraud, etc. Although artificial intelligence (AI) financial chatbots and robo-advisory services have sped up the pace of service and lowered operating costs, they will be ineffective if customers do not trust them; they do not believe that their risks are well-managed; there is a lack of transparency in management, and a sense of alignment among the parties is missing. The above theory can help us understand why people have been using AI-driven financial advisory chatbots in the era of digital finance for a long time. According to the Technology Acceptance Model, based on the concepts of electronic service quality, trust frameworks and research on AI adoption, it has been proposed that AI service engagement and financial data quality affect perceived utility, perceived usability, perceived risk, trust and continuance behaviour. The choice of financial services contains one's personal data, investment risk, etc., and may not be convenient for one's own interests or the normal development of business. The first three reasons for this paper are as follows. First, it expands the scope of research on the quality of AI chatbot service from commercial and public service sectors to financial administration and the environment of FinTech services. Second, there is the concept of risk and openness in the Trust-Centered Technology Acceptance Model. Thirdly, it provides some helpful ideas for banks, securities companies, FinTech enterprises and platform operators on how to build stable, transparent and convenient AI financial service platforms. Empirical studies using a survey-based structural equation model will be conducted in the next paper of this journal to verify the new model.

Yiming Li. Trust-Based Acceptance of AI Financial Advisory Chatbots: A Conceptual Study of Service Quality, Perceived Risk, and Continuance Intention. International Journal of Business Management and Economics and Trade (2026), Vol. 7, Issue 1: 157-166. https://doi.org/10.38007/IJBMET.2026.070118.

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