AI-Powered Customized Banking Experiences: Using Data Science to Provide Tailored Financial Services

Personalized banking experiences are rapidly transforming the financial services industry by catering to individual customer needs, preferences, and behaviors. This paper presents a comprehensive study on the integration of artificial intelligence (AI) and data science techniques to enable hyper-personalization in retail and corporate banking. We first review existing personalization strategies and key challenges in data collection, privacy, and algorithmic fairness [1], [2]. Building on this foundation, we introduce a modular framework that combines advanced data preprocessing, feature engineering, machine learning, deep learning, and reinforcement learning to deliver tailored recommendations, dynamic pricing, risk scoring, and proactive financial health insights [3], [4]. We demonstrate the efficacy of the framework through two case studies: real-time loan eligibility scoring [10], [11] and personalized investment portfolio optimization [5], [11]. Finally, we discuss operational considerations, ethical implications, and future research directions to guide both academics and industry practitioners in deploying responsible, scalable, and secure personalized banking services [6], [9].