Guided by the framework of Factors of Trust Development in Human–Robot Interaction (FTD-HRI) and theories of interpersonal trust, this study examines how trust—conceptualized as a social script—influences users’ intentions to engage with financial chatbots embedded in mobile banking applications. Situated within the field of human–machine communication (HMC), the research identifies key predictors of trust and clarifies the mediating role of trust in the relationship between these predictors and usage intention. A questionnaire survey (N = 536) combined with partial least squares structural equation modeling (PLS-SEM) yields the following findings: (a) trust is positively associated with users’ intention to use financial chatbots; (b) among trust predictors, cognitive factors—perceived ease of use, perceived expertise, and risk perception—show significant effects, with the first two positively and the latter negatively associated with trust, while affective factors—attitude toward technology and perceived human-likeness—display inverse patterns; (c) trust significantly mediates the effects of these predictors on usage intention; and (d) task complexity directly influences intention to use. The study discusses implications for the adoption of chatbots across domains such as finance, healthcare, and smart transportation, framing trust as a socially embedded process in HMC.