Key Drivers and Machine Learning in Personalized Mobile Banking - Opportunities and challenges for retail banks

Abstract

This thesis explores the integration of AI-driven personalization into retail banks' mobile banking services. It investigates the opportunities and challenges arising from this convergence as retail banks seek to fulfil customer expectations for convenience and engagement in the retail financial sector. Consumer expectations for personalized services are on the rise, creating a dynamic where companies must navigate the balance between personalization and cost-effectiveness in the development of mobile banking. To comprehensively analyse this, a broad literature review is chosen as the research method.

The findings reveal that AI-powered personalization holds promise in enhancing user engagement but poses challenges related to scalability. Tailoring strategies to the unique needs and preferences of each customer segment is crucial. Transparency, user control and user empowerment are key factors in building trust in AI-driven financial services. In summary, this thesis offers insights into the potential of applying machine learning algorithms to enrich and personalize the mobile banking experience for retail banks, giving insights to retail banks, and researchers alike.