The rapid digital transformation in financial services has made user behavioral data one of the most valuable assets for understanding and predicting financial decision-making. In this context, financial recommender systems have emerged as key tools for personalizing services, enhancing user experience, and improving investment decision efficiency. However, most existing systems still rely primarily on static financial data or limited user attributes, failing to fully incorporate behavioral, cognitive, and digital interaction patterns into recommendation processes. This study aims to develop a personalized financial recommender system framework based on digital behavioral analytics of customers. Using a conceptual-analytical approach and drawing on artificial intelligence, machine learning, and behavioral economics literature, the research proposes a multi-layered model for analyzing user behavior in digital financial environments. Behavioral data such as clickstream patterns, interaction time, transaction history, engagement with financial products, and digital psychological indicators are extracted and utilized as inputs for the recommender model. The findings indicate that integrating digital behavioral analytics with recommendation algorithms significantly enhances the accuracy of financial need prediction and improves the level of service personalization. Furthermore, this approach contributes to reducing cognitive biases in retail investors’ decisions and increasing overall market efficiency. The study ultimately provides a novel framework for the next generation of financial recommender systems in which behavioral data and intelligent algorithms are seamlessly integrated into financial decision-making processes.