AI-Based Customer Behavior Prediction in Digital Banking and FinTech Platforms

Abstract

Digital banking and FinTech platforms produce detailed behavioral data from every login, tap, payment, transfer, and support interaction. Artificial intelligence (AI) lets institutions turn this data into predictions of what customers will do next: whether they will leave, which products they will adopt, how they will repay credit, how valuable they will become, and when they may need help. This review synthesizes research on AI-based customer behavior prediction in digital financial services, organized around three themes: (1) the predictive techniques used for churn, customer lifetime value, product propensity, credit and repayment behavior, and engagement, from ensemble learning to sequence models, recommender systems, and uplift modeling; (2) the operational and architectural foundations that turn predictions into timely action, including configurable workflows, metadata-driven platforms, zero-downtime model deployment, and reliable digital channels; and (3) the growing use of large language models (LLMs) in customer-facing banking services, together with the prompt engineering, cost management, and guardrails they require. The evidence shows that machine learning (ML) models, especially tree-based ensembles and models trained on fine-grained transaction and interaction data, predict customer behavior more accurately than traditional segmentation and regression approaches. Their business value, however, depends on acting on the right customers, adapting to changing behavior, and meeting expectations for privacy, fairness, transparency, and safety. The review proposes a layered framework for AI-driven customer behavior management and identifies directions for future research.