Predictive Analytics and the Role of AI-Powered Robo-Advisers in Modern Banking Opportunities and Challenges

The paper tries to analyze a study on how AI-powered robo-advisers are changing the face of banking in the modern world with the potential democratization of financial services by giving more personalized, efficient, and cost-effective solutions. Based on predictive analytics and machine learning, this study will look at some important factors that can influence customer readiness, adoption, and performance of robo-advisors. Respondents in this survey are very diverse and are administered with the help of a structured questionnaire. The data is then analyzed using advanced techniques, including decision trees, clustering, and regression. The key findings underline the main drivers of adoption as digital literacy, trust in AI, cost, and personalization, while the main barriers identified include privacy concerns, lack of trust, and usability challenges. Hierarchical clustering provided insights that clear customer groups with different concerns could be distinguished, rendering banks actionable strategies to address these barriers. This research has shown the improvement in customer education, building of trust, and cost-effectiveness as the main accelerators to increase the adoption of robo-advisers. These findings add to the burgeoning literature on AI in banking and inform future research directions.