AI-POWERED PERSONALIZATION IN DIGITAL BANKING: A REVIEW OF CUSTOMER BEHAVIOR ANALYTICS AND ENGAGEMENT

Abstract

The rapid evolution of digital banking has prompted financial institutions to integrate artificial intelligence (AI) technologies to deliver highly personalized and engaging customer experiences. As customer expectations grow increasingly dynamic, AI-powered personalization has emerged as a strategic imperative, enabling banks to tailor services in real time based on individual behaviors, preferences, and financial patterns. This study systematically reviews the literature on AI-powered personalization in digital banking, with a specific focus on how customer behavior analytics and intelligent algorithms contribute to enhanced engagement, satisfaction, retention, and trust. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 framework, a total of 111 peer-reviewed articles published between 2014 and 2024 were analyzed to identify core themes, methodologies, innovations, and conceptual gaps. The reviewed literature is thematically organized into seven key domains: foundational AI techniques, behavioral data modeling, predictive analytics, customer engagement outcomes, ethical and governance challenges, innovations in emerging markets, and research limitations. The findings reveal that AI-driven personalization not only improves operational efficiency and service quality but also fosters emotional loyalty and increases the lifetime value of banking customers. Advanced AI techniques—such as machine learning, natural language processing, recommender systems, and sentiment analysis—are widely applied to deliver seamless, context-aware experiences across mobile apps, web portals, and virtual assistants. However, the literature also highlights significant challenges, including inconsistent measurement frameworks, regulatory uncertainty, data privacy concerns, and insufficient attention to cultural diversity and longitudinal performance. Emerging markets, while constrained by infrastructural and regulatory limitations, exhibit innovative adaptations through alternative data use and hybrid AI-human service delivery models. This review offers a comprehensive synthesis of the academic discourse on AI personalization in digital banking and underscores critical areas for future research, industry practice, and policy intervention aimed at building inclusive, ethical, and scalable AI solutions.