The growing use of artificial intelligence in banking has altered the way customers experience digital financial services, particularly through personalised recommendations, alerts and service support. This study examines customer satisfaction in the context of AI-driven personalised banking with specific reference to perceived usefulness, convenience, trust, privacy and service personalisation. A descriptive and empirical research design was adopted, supported by a quantitative approach. Primary data were collected from 325 banking customers through a structured questionnaire comprising 30 Likert-scale statements across six constructs. Reliability analysis confirmed acceptable internal consistency for all constructs, with Cronbach’s alpha values ranging from 0.865 to 0.881. Multiple linear regression was used to test the proposed hypothesis. The results showed that perceived usefulness, convenience, trust, privacy and service personalisation jointly explained 54 percent of the variance in customer satisfaction, with the model showing statistical significance. Convenience emerged as the strongest predictor, while all other variables also showed positive and significant contributions. The study concludes that customer satisfaction in AI-driven personalised banking depends not merely on technology adoption, but on how customers experience usefulness, ease, trust, privacy assurance and personal relevance in digital banking services.