Developing a prediction model for customer churn from electronic banking services using data mining

Abstract

Background: Given the importance of customers as the most valuable assets of organizations, customer retention seems to be an essential, basic requirement for any organization. Banks are no exception to this rule. The competitive atmosphere within which electronic banking services are provided by different banks increases the necessity of customer retention. Methods: Being based on existing information technologies which allow one to collect data from organizations' databases, data mining introduces a powerful tool for the extraction of knowledge from huge amounts of data. In this research, the decision tree technique was applied to build a model incorporating this knowledge. Results:The results represent the characteristics of churned customers. Conclusions: Bank managers can identify churners in future using the results of decision tree. They should be provide some strategies for customers whose features are getting more likely to churner's features.

Digitalisation and Big Data Mining in Banking

“…Customer retention and acquisition [69][70][71][72][73][74][75] classification (DT, NN, LR, SVM), ARM, k-mean clustering EU [69], China [70], Nigeria [71], Croatia [73], Bangladesh [75] Customer churn prediction and prevention, attracting potential customers and strategic future service design.…”

“…Data relating to private banking customers in a European bank was analyzed in [69]; He et al [70] employed the SVM technique for customer churn and attrition prediction with a real life data set from a Chinese commercial bank; customer records of a major bank in Nigeria were investigated in [71]; later, in [72] a NN classification technique was applied on the customer database of an international bank for customer churn prediction; similar research was also conducted in a small Croatian bank in [73] and the electronic banking service data set in [74].…”

Churn rate prediction in Telecommunication systems

“…Classifications apparatuses are frequently used to display and anticipate client agitate. A portion of the procedures usually used to accomplish this are neural networks, Decision tree (DT), accidental forests, support vector machines (SVM) and logistic corruption [5].…”

Predicting Potential Banking Customer Churn using Apache Spark ML and MLlib Packages: A Comparative Study

“…Churn prediction [3] [4] is the process of predicting the intention of customers to leave. It is one of the most debated researches in last years.…”