Enhancing Customer Trust in Indonesian Digital Banking: The Moderating Role of Data Privacy Disclosure on AI-Driven Interactions

Abstract

Purpose: This study examines how AI-enabled banking interactions influence customer trust and whether data privacy disclosure strengthens this relationship among SeaBank users in DKI Jakarta, Indonesia. Design/methodology/approach: Drawing on the Stimulus-Organism-Response (S-O-R) framework and Social Identity Theory, the study employed an explanatory quantitative design. Data were collected through a structured online questionnaire from 180 active SeaBank depositors aged 18-35 years who had used AI-supported banking features at least three times in the previous three months. The data were analyzed using Ordinary Least Squares (OLS) regression and Moderated Regression Analysis (MRA). Findings: The results demonstrate that AI-enabled interaction has a positive and significant effect on customer trust, while data privacy disclosure also directly enhances trust. More importantly, the interaction term is positive and significant, indicating that transparent privacy disclosure strengthens the relationship between AI-enabled interaction and customer trust. The moderated model explains 56.4% of the variance in customer trust, with the interaction term adding 8.2% incremental explanatory power. Originality/value: This study advances digital banking trust literature by showing that AI-enabled service quality alone is insufficient to generate trust. In an emerging-market context, algorithmic convenience must be accompanied by visible, understandable, and controllable privacy disclosure. The findings extend the S-O-R framework by conceptualizing privacy disclosure as a trust-enabling boundary condition and offer actionable guidance for digital banks seeking to transform privacy governance into a competitive advantage.