Customer Churn Analysis and Prediction Using Data Mining Models in Banking Industry

Ketut Gde Manik Karvana · Setiadi Yazid · Amril Syalim · Petrus Mursanto

50 citations0/0 files storedInternational Workshop on Big Data and Information Security (IWBIS)

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Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

Abstract

A new method for customer churn analysis and prediction has been proposed. The method uses data mining model in banking industries. This has been inspired by the fact that there are around 1,5 million churn customers in a year which is increasing every year. Churn customer prediction is an activity carried out to predict whether the customer will leave the company or not. One way to predict this customer churn is to use a classification technique from data mining that produces a machine learning model. This study tested 5 different classification methods with a dataset consisting of 57 attributes. Experiments were carried out several times using comparisons between different classes. Support Vector Machine (SVM) with a comparison of 50:50 Class sampling data is the best method for predicting churn customers at a private bank in Indonesia. The results of this modeling can be utilized by company who will apply strategic action to prevent customer churn.

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1997Decision support systems and intelligent systems 2018A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees 2015A comparison of machine learning techniques for customer churn prediction 2016Developing a prediction model for customer churn from electronic banking services using data mining 2016Predicting Customer Churn in Banking Industry using Neural Networks 2017Designing of customer and employee churn prediction model based on data mining method and neural predictor

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