Abstract

While AI-enabled FinTech services promise greater efficiency, personalization, and cost savings, users may not evaluate these systems solely based on objective product features. Instead, their judgments may be filtered through preexisting attitudes toward AI—what we describe as “AI feelings.” Drawing on research on technology acceptance, trust in automation, perceived risk, and price fairness, this study distinguishes between product-level cues, including risk, monetary benefit, and AI presence, and users’ baseline orientations toward AI. We argue that adoption depends not only on what the platform communicates but also on the psychological lens users bring to the evaluation. To test this framework, we conducted a pre-registered 2 × 2 × 2 between-subjects online experiment with 842 Prolific participants. Participants evaluated a hypothetical healthcare-oriented FinTech platform designed to resemble real-world medical payment services. The platform varied in risk cues, operationalized through positive versus mixed/negative user reviews; benefit cues, operationalized through advertised medical savings of 50% versus 5%; and AI presence, operationalized through an AI advisor versus a human advisor. We examined the effects of these cues on cognitive trust, affective trust, perceived price fairness, perceived usefulness, perceived ease of use, and intention to adopt the platform. Preliminary results show that risk cues were the strongest and most consistent drivers of trust and adoption. High-risk cues significantly reduced cognitive trust, affective trust, and intention to use the platform. Benefit cues increased perceived price fairness and adoption intention, suggesting that savings messages operate primarily through a value and fairness pathway rather than directly building trust. Interestingly, contrary to common assumptions about algorithm aversion, the mere presence of AI did not significantly affect affective trust or adoption intention. However, baseline AI attitudes significantly predicted adoption-related outcomes indirectly through cognitive trust, affective trust, and perceived usefulness. These findings support an “attitudes-as-lens” account: users do not evaluate AI-enabled services from scratch but interpret platform cues through preexisting affective orientations toward AI. This study contributes to research on AI trust, FinTech adoption, and healthcare-related consumer technologies by clarifying the relative roles of objective platform cues and subjective AI attitudes. Practically, the findings suggest that organizations should not rely on AI labeling or savings claims alone. Successful implementation of AI-enabled healthcare FinTech requires coordinated strategies that reduce perceived risk, communicate fairness and value, and build user confidence in AI-mediated decision support.

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