Artificial Intelligence (AI) has revolutionized the field of financial technology (FinTech), reshaping how financial services are delivered to consumers. While AI-powered FinTech presents significant technological opportunities, consumer adoption rates are still not high enough, largely because of ongoing trust and security issues. This research builds and tests an integrated theoretical model to understand the relationship among AI system trust, perceived financial data security, and overall behavioral intention towards AI-powered FinTech services. Based on the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Trust Transfer Theory, we propose a multi-dimensional model that includes cognitive trust, affective trust, system security assurance, data privacy protection and perceived risk as antecedents of adoption intentions. A cross-sectional survey was carried out on a structured questionnaire with 487 urban consumers in three metros of India (New Delhi, Mumbai and Bangalore). Structural equation modeling (SEM) using partial least squares (PLS) was used for hypothesis testing. The results show that cognitive trust (β = 0.312, p < 0.001) and perceived security assurance (β = 0.278, p < 0.001) have the most significant direct impacts on the behavioral intentions, whereas affective trust mainly acts as a mediator variable. The results show that perceived risk has a significant negative moderating effect on the trust-intention relationship (β = -0.156, p < 0.01). Moreover, the study shows that there are significant demographic differences; younger consumers (18-30-years-old) had higher risk tolerances and greater intentions of adoption than older consumers (β = 0.189, p < 0.05). The study advances the theory of consumer behavior by adding a dual cognitive-affective dimension to the trust construct in the context of FinTech, and offers valuable implications for practitioners aiming to boost consumer trust in AI-based financial services..