Behavior-Driven Personalization Framework to Improve Repeat Usage in Mobile-Enabled Financial Ecosystems

As mobile-enabled financial platforms proliferate globally, improving user retention has become a critical challenge for product teams and fintech providers. While initial adoption is high, sustained engagement remains limited due to the generic and non-responsive nature of many user experiences. This paper proposes a behavior-driven personalization framework designed to enhance repeat usage within mobile financial ecosystems by leveraging real-time user behavior data. Grounded in behavioral science principles such as heuristics, loss aversion, and choice architecture, the framework consists of three core components: behavioral data capture, a dynamic segmentation engine, and a personalized trigger system. Together, these layers facilitate the delivery of contextualized interventions, such as smart notifications, adaptive UI prompts, and in-app nudges, that align with users' financial behavior and goals. The framework is supported by a continuous learning loop that refines personalization strategies based on observed user responses, enabling mobile financial applications to become increasingly responsive and user-centered over time. Implementation guidelines include scalable data infrastructure, integration across the product lifecycle, and metrics that go beyond standard retention, such as feature recurrence and financial goal progression. Ethical considerations are embedded throughout, emphasizing transparency and responsible design. The paper concludes by outlining future research opportunities in hybrid psychographic-behavioral models and the governance of predictive personalization in finance.