AI-Driven Personalisation in E-Banking Services: A Study of Customer Experience in Indian Commercial Banks
DOI:
https://doi.org/10.70917/ijcisim-2026-3077Keywords:
AI personalisation, e-banking, customer experience, SEM, XGBoost, SHAP, Indian commercial banks, predictive credit scoring, chatbot, digital financial literacyAbstract
The rapid integration of artificial intelligence into Indian commercial banking has created an unprecedented personalisation infrastructure spanning AI-driven product recommendations, hyper-personalised communication, predictive credit scoring, intelligent conversational agents, privacy-preserving data analytics, and omnichannel continuity systems. Yet the structural mechanisms through which these AI personalisation capabilities translate into measurable customer experience quality and downstream loyalty, cross-sell, and word-of-mouth outcomes remain empirically underspecified in the Indian banking context. This study examines the impact of six AI personalisation dimensions on customer experience quality in Indian commercial banks through a survey of N = 532 retail banking customers across five bank category types. A three-stage analytical framework of Confirmatory Factor Analysis, Structural Equation Modeling, and XGBoost machine learning is deployed. CFA confirms a six-factor measurement model with excellent fit (CFI = .963, RMSEA = .041). SEM establishes AI product recommendation quality (beta = .47, p < .001) and hyper-personalised communication (beta = .43, p < .001) as dominant customer experience drivers, with digital financial literacy significantly moderating all six AI personalisation pathways. XGBoost achieves R squared = .843, identifying contextually precise product recommendation, timely personalised alerts, and transparent AI credit scoring as the three highest-impact SHAP predictors. A recommendation quality customer experience acceleration point at 79 percent is identified. Privacy-preserving personalisation demonstrates full mediation of its effect on word-of-mouth intention. Foreign banks demonstrate the strongest AI personalisation effects while public sector banks show the greatest investment opportunity gap. Findings advance S-O-R Theory, TAM, and ECM in AI-mediated banking contexts and provide actionable guidance for bank CX architects and RBI digital banking policy designers.
