Prediction of Customer Churn in Banking Industry

TL;DR

This paper compares six supervised classification methods to predict customer churn in European banks, highlighting the effectiveness of ANN with five hidden nodes as the best model considering feature selection, class imbalance, and noise robustness.

Contribution

It introduces a comparative analysis of multiple classifiers for banking customer churn prediction, emphasizing the robustness of ANN over random forest.

Findings

ANN with five hidden nodes outperforms other models.

ANN is more robust to noise and overfitting than random forest.

Feature selection and class imbalance significantly affect model performance.

Abstract

With the growing competition in banking industry, banks are required to follow customer retention strategies while they are trying to increase their market share by acquiring new customers. This study compares the performance of six supervised classification techniques to suggest an efficient model to predict customer churn in banking industry, given 10 demographic and personal attributes from 10000 customers of European banks. The effect of feature selection, class imbalance, and outliers will be discussed for ANN and random forest as the two competing models. As shown, unlike random forest, ANN does not reveal any serious concern regarding overfitting and is also robust to noise. Therefore, ANN structure with five nodes in a single hidden layer is recognized as the best performing classifier.