ArticleScientific reports2026
An AI-driven framework for cybersecurity awareness and user trust in indian digital banking.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The rapid expansion of digital banking in India has heightened concerns regarding cybersecurity, significantly influencing user trust in online financial systems. This study examines the relationships among cybersecurity awareness, perceived threats, Institutional Educational Support, and user trust in e-banking, while proposing an AI-enhanced trust framework. Data were collected from over 500 university students across India using a Computer-Assisted Web Interviewing (CAWI) approach. A multi-method analytical framework is employed, combining statistical modeling with artificial intelligence (AI)-based predictive analytics. Structural Equation Modeling (SEM) using AMOS was employed to examine the relationships among the constructs. Correlation and multiple regression analyses were conducted as preliminary analyses, while machine learning models including Random Forest, Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Linear Regression are applied to predict trust levels and validate model robustness. AI is operationalized both as a perceptual construct measured through survey-based indicators and as a predictive analytical approach using machine learning techniques. The results indicate that cybersecurity awareness (β = 0.58) and Institutional Educational Support (β = 0.33) positively influence user trust, while perceived threats (β = - 0.27) negatively affect trust, with the model explaining approximately 69% of the variance. Mediation analysis shows that awareness mediates the relationship between education and trust, while moderation analysis reveals that AI perception strengthens the awareness-trust relationship. Among predictive models, Random Forest achieved the highest accuracy (approximately 82%), highlighting the effectiveness of AI-driven approaches. Additionally, machine learning models were applied to enhance predictive analysis of user trust. The study contributes by integrating behavioral, institutional, and AI-driven perspectives, offering practical implications for enhancing trust in digital banking systems.
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