Data Mining and Machine Learning Techniques for Bank Customers Segmentation: A Systematic Mapping Study

Abstract

Data mining and machine learning techniques analyze and extract useful information from data sets in order to solve problems in different areas. For the banking sector, knowing the characteristics of customers entails a business advantage since more personalized products and services can be offered. The goal of this study is to identify and characterize data mining and machine learning techniques used for bank customer segmentation, their support tools, together with evaluation metrics and datasets. We performed a systematic literature mapping of 87 primary studies published between 2005 and 2019. We found that decision trees and linear predictors were the most used data mining and machine learning paradigms in bank customer segmentation. From the 41 studies that reported support tools, Weka and Matlab were the two most commonly cited. Regarding the evaluation metrics and datasets, accuracy was the most frequently used metric, whereas the UCI Machine Learning repository from the University of California was the most used dataset. In summary, several data mining and machine learning techniques have been applied to the problem of customer segmentation, with clear tendencies regarding the techniques, tools, metrics and datasets.