Predictive Customer Retention Models for Fintech Using Cloud AI Tool chains

Abstract

Customer retention has emerged as a critical determinant of long-term profitability and sustainability in fintech ecosystems characterized by low switching costs, intense competition, and rapidly evolving digital services. While fintech platforms generate vast volumes of behavioral, transactional, and contextual data, many organizations struggle to translate these signals into actionable retention strategies. Predictive analytics powered by cloud-based artificial intelligence (AI) toolchains offer a scalable and adaptive approach to identifying churn risk and proactively engaging customers. This paper examines predictive customer retention models for fintech products, focusing on how cloud AI toolchains enable end-to-end lifecycle management from data ingestion and feature engineering to model deployment and continuous learning. Through architectural synthesis, model taxonomy analysis, and expert-informed evaluation, the study proposes a cloud AI retention intelligence framework that integrates machine learning models, real time analytics, and product decision systems. The findings demonstrate that predictive retention models significantly improve churn forecasting accuracy, enable personalized intervention strategies, and enhance customer lifetime value without compromising data governance or regulatory compliance. The paper positions predictive retention modeling not merely as a marketing analytics function, but as a core product capability essential for scalable, customer-centric fintech growth.