AI-Driven Personalization and Data-Centric Marketing in Indian FinTech: Empirical Evidence on Customer Satisfaction, Trust, and Engagement

Abstract

The rapid proliferation of Financial Technology (FinTech) has fundamentally transformed customer engagement paradigms in the Indian financial services sector. This study empirically investigates the influence of Artificial Intelligence (AI)-driven personalization and automation tools on customer satisfaction, trust, and engagement among Indian FinTech users. Grounded in the Technology Acceptance Model (TAM), UTAUT2, Privacy Calculus Theory, and Information Systems Trust Theory, the research adopts a quantitative cross-sectional design. Primary data were collected from 200 FinTech users across India using a structured Likert-scale questionnaire spanning eight constructs. Statistical analyses comprising one-sample t-tests, Pearson correlation analysis, and one-way ANOVA were executed using SPSS. Key findings reveal that AI personalization exerts the strongest positive influence on customer satisfaction (r = 0.706, p < 0.01) and trust (r = 0.671, p < 0.01). Automation exposure significantly correlates with engagement (r = 0.438, p < 0.01). Transparency positively moderates trust (r = 0.478, p < 0.01), while privacy risk demonstrates only a weak negative effect on trust (r = −0.152, p < 0.05) without influencing satisfaction. Digital literacy exhibits no significant moderating effect (r = −0.064, p > 0.05), indicating that contemporary FinTech interfaces have democratized AI benefits across literacy strata. The study validates three of five hypotheses and contributes novel context-specific evidence to the global FinTech personalization literature, offering actionable implications for practitioners and policymakers.