Predicting Customer Churn in Banking Industry using Neural Networks

Abstract

The aim of this article is to present a case study of usage of one of the data mining methods, neural network, in knowledge discovery from databases in the banking industry. Data mining is automated process of analysing, organization or grouping a large set of data from different perspectives and summarizing it into useful information using special algorithms. Data mining can help to resolve banking problems by finding some regularity, causality and correlation to business information which are not visible at first sight because they are hidden in large amounts of data. In this paper, we used one of the data mining methods, neural network, within the software package Alyuda NeuroInteligence to predict customer churn in bank. The focus on customer churn is to determinate the customers who are at risk of leaving and analysing whether those customers are worth retaining. Neural network is statistical learning model inspired by biological neural and it is used to estimate or approximate functions that can depend on a large number of inputs which are generally unknown. Although the method itself is complicated, there are tools that enable the use of neural networks without much prior knowledge of how they operate. The results show that clients who use more bank services (products) are more loyal, so bank should focus on those clients who use less than three products, and offer them products according to their needs. Similar results are obtained for different network topologies.

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].…”

Experimental Analysis of Hyperparameters for Deep Learning-Based Churn Prediction in the Banking Sector

“…By keeping regular checks on customers' transaction statuses, banks generate a huge amount of data, which makes it difficult for them to compute and obtain meaningful knowledge from it using traditional statistical methods [17,18]. This necessitated the development of powerful algorithms that use machine learning techniques to discover hidden patterns and predict behaviors and the likelihood of a customer unsubscribing from an organization's services [4]. A host of studies have noted that it is more expensive to acquire new customers than spend to retain existing ones [7,16].…”

“…Data mining methods such as machine learning techniques are now being used to predict customer churn in competitive organizations and to discover hidden patterns that were previously too complex and time-consuming to uncover at first sight [4,9]. When machine learning algorithms are trained with valid data about customers' transactions, useful knowledge in the data is discovered, and challenges in the bank are resolved by finding some regular patterns, causality, and correlation with business information.…”

“…Data by themselves do not have much value if they are not studied to reveal the information contained in them. To find valuable information from data, a process called data mining is applied [4]. Machine learning, a subset of data mining, allows organizations to study customers' behaviors, 2 of 19 including churn [5].…”