Enhancing User Engagement in Mobile Banking Through Personalized Recommendation Algorithms

In the context of increasing reliance on digital financial services, mobile banking applications have become essential platforms for customer engagement. Despite their widespread adoption, maintaining consistent user interaction remains a significant challenge for financial institutions. This study investigates the effectiveness of personalized recommendation algorithms in enhancing user engagement within mobile banking environments. The research aims to evaluate how individualized content suggestions influence key user behavior metrics, including satisfaction, continued usage, and perceived relevance. A quantitative approach was employed using Structural Equation Modeling (SEM) based on survey data from 231 mobile banking users in Indonesia, supplemented by expert interviews to contextualize the findings. The results demonstrate that personalized recommendations have a statistically significant positive effect on user engagement and satisfaction, supporting the notion that tailored digital experiences can strengthen customer relationships in financial technology platforms. However, concerns related to data privacy and algorithmic fairness were identified as critical issues that must be addressed to sustain long-term user trust. These findings suggest that integrating personalized algorithms into mobile banking interfaces can serve as a strategic enhancement to user experience, provided that ethical and transparency concerns are concurrently managed. Future research should expand the geographic scope, incorporate objective behavioral analytics, and explore longitudinal effects to further validate the impact of personalization technologies in financial applications.