Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
An introduction to variable and feature selection
The Journal of Machine Learning Research
Revenue recovering with insolvency prevention on a Brazilian telecom operator
ACM SIGKDD Explorations Newsletter
Review: Neural networks and statistical techniques: A review of applications
Expert Systems with Applications: An International Journal
The individual borrowers recognition: Single and ensemble trees
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
In this article, we test several models (decision tress and neural networks) to predict customer insolvency at one of the cellular telecommunication operator in Poland. In comparison to previous studies on customer insolvency, our research presents novelty in the following areas. First of all, we deal with cellular telecommunication customers who use services without paying dues (the literature show the studies that dealt mainly with fixed line operators or banking industry). Secondly, we have a large dataset with 205 explanatory variables derived from the database (previous studies dealt with smaller datasets). Thirdly, we test the quality of the models three months after the estimation with lift and ROC curves to assess the models performance over the time. The main findings from the research are twofold: (1) artificial neural networks perform well when modeling the customer's insolvency and the best model can capture significant amount of money owed by insolvent customers; (2) decision trees get old quickly and their performance decrease over the time in the top percentiles of the score.