Empowering Smart Cities with AI: Predictive Models for Customer Retention in Banking

Abstract

Introduction: Smart cities thrive on innovative technologies, and artificial intelligence (AI) plays a pivotal role in enhancing customer-centric services. In the context of the banking sector, customer retention is vital for maintaining competitiveness, especially in the highly dynamic urban environments of smart cities.

Objectives: The main objective of this study is to investigate the application of supervised machine learning algorithms to predict customer churn, a critical factor in developing efficient retention strategies.

Methods: This work uses a dataset of 10,000 customer records, models such as Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, Gradient Boosting, LightGBM, XGBoost, and Naive Bayes were evaluated. Preprocessing and analysis were conducted with key metrics including accuracy, precision, recall, F1-score, cross-validation, and AUC-ROC.

Results: The results reveal that ensemble models, particularly Gradient Boosting, XGBoost, Random Forest, and LightGBM, deliver superior performance on unbalanced data, achieving accuracies of 85.65%, 85.65%, 85.25%, and 85.35%, respectively.

Conclusion: On balanced data, LightGBM outperformed others with an accuracy of 84.21%. These findings highlight the potential of AI-driven predictive models to empower banking institutions in smart cities, fostering better customer retention and contributing to sustainable urban development.