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Nowadays, customer analytics plays a fundamental role in the commercial activity of a financial company, which is interested to deliver the best products and services to consumers. However, sometimes things may possibly go wrong, resulting in inconveniences experienced by the clients, who may eventually decide to end the relationship with a bank. This study uses a dataset which contains a bank’s customer base who churned, with a series of variables regarding sociodemographic characteristics, member activity, balance, estimated salary and tenure. An issue of general interest in banking field is predicting the probability of churn or attrition, a phenomenon which may lead to significant decreasing of revenues if the company fails to act on time. Therefore, in this paper we examine which are the main characteristics of customers that influence the propensity to churn by means of an exploratory data analysis. Moreover, we employ two machine learning algorithms and use an optimization technique called Grid Search to obtain the optimal predictive model, which can estimate the likelihood of a customer leaving a bank in the future. Ultimately, we use the Area Under the Curve (AUC) as model performance metric to compare the outcomes of Logistic Regression and Random Forest classifiers on the test dataset. Final results show that Random Forest classifier outperformed Logistic Regression model in terms of AUC values and, at the same time,the outcomes emphasize different behaviors between countries.

Propensity to Churn in Banking: What Makes Customers Close the Relationship with a Bank?