The advent of generative models has emerged as a fundamental shift of perspective in modern machine learning, with the potential to revolutionize a variety of domains. The generation of data that closely resembles real-world events has enabled the transformation of various businesses, increasing the possibility of building newer and more successful ones. In this work, we demonstrate the design and implementation of a machine-learning pipeline for financial CRM (Customer Relationship Management), specifically for predicting credit customer churning. The developed end-to-end pipeline proposes a predictive model with a high predictive capacity of recognizing churning behaviours that adheres to transparency and discriminant-free paradigms.