Supervisor
Taufique Ahmed
Programme
HDIP in Data Analytics for Business
Subject
Computer Science
Abstract
Customer churn, when customers stop using a company’s services, is a challenge for the banking sector (Singh et al., 2023). High churn rates often signal poor customer experiences, resulting in revenue losses and increased costs to obtain new clients. Goyal and Srivastava (2015) stress that fostering loyalty through exceptional service and understanding customer needs is important for long-term retention.
This project aims to predict early customer inactivity, an indicator of churn, by using machine learning. Early identification of at-risk customers will allow banks to apply targeted interventions, reduce acquisition costs, and improve customer satisfaction (Singh et al., 2023). By analysing a dataset containing customer demographics, financial activity, and behavioural patterns, this project provides insights that align with banks strategic goals of improving profitability and customer loyalty.
The CRISP-DM methodology (Cross-Industry Standard Process for Data Mining) will guide this project. This structured, iterative framework ensures a systematic progression through key phases: business understanding, data understanding, data preparation, modelling, evaluation, and deployment. The methodology ensures alignment with business objectives and allows for iterative refinement, ensuring robust and actionable outcomes (Hotz, 2024).
Date of Award
2025
Full Publication Date
2025
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
Mc Fadden, I. (2025) Predicting Early Customer Inactivity in the Banking Sector Using Machine Learning: A Churn Prevention CCT College Dublin.