Supervisor
Taufique Ahmed
Programme
HDIP in Data Analytics for Business
Subject
Computer Science
Abstract
This project presents a customer churn prediction analysis in the telecommunications sector, achieving an ROC-AUC of approximately 0.86 using statistically validated features and interpretable AI models. Key churn drivers identified include the number of products held, customer age, and geographic location. Ensemble models, such as Random Forest and Gradient Boosting, provided the highest predictive performance. Ethical AI principles were applied to ensure fairness, transparency, privacy, and accountability. Business insights derived from the analysis inform targeted retention strategies, prioritising multi-product users, specific age groups, and geographic segments. Deployment recommendations include the tuned Random Forest model with ongoing monitoring, governance, and future enhancements for feature expansion, interpretability, and real-time integration.
Date of Award
2025
Full Publication Date
2025
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
Thiha, T.
(2025) Predictive Analytics for Customer Churns in Financial Services. CCT College Dublin.
DOI: https://doi.org/10.63227/652.299.77