Network-aware credit scoring system for telecom subscribers using machine learning and network analysis
Asia Pacific Journal of Marketing and Logistics
ISSN: 1355-5855
Article publication date: 5 October 2021
Issue publication date: 1 April 2022
Abstract
Purpose
A good decision support system for credit scoring enables telecom operators to measure the subscribers' creditworthiness in a fine-grained manner. This paper aims to propose a robust credit scoring system by leveraging latent information embedded in the telecom subscriber relation network based on multi-source data sources, including telecom inner data, online app usage, and offline consumption footprint.
Design/methodology/approach
Rooting from network science, the relation network model and singular value decomposition are integrated to infer different subscriber subgroups. Employing the results of network inference, the paper proposed a network-aware credit scoring system to predict the continuous credit scores by implementing several state-of-art techniques, i.e. multivariate linear regression, random forest regression, support vector regression, multilayer perceptron, and a deep learning algorithm. The authors use a data set consisting of 926 users of a Chinese major telecom operator within one month of 2018 to verify the proposed approach.
Findings
The distribution of telecom subscriber relation network follows a power-law function instead of the Gaussian function previously thought. This network-aware inference divides the subscriber population into a connected subgroup and a discrete subgroup. Besides, the findings demonstrate that the network-aware decision support system achieves better and more accurate prediction performance. In particular, the results show that our approach considering stochastic equivalence reveals that the forecasting error of the connected-subgroup model is significantly reduced by 7.89–25.64% as compared to the benchmark. Deep learning performs the best which might indicate that a non-linear relationship exists between telecom subscribers' credit scores and their multi-channel behaviours.
Originality/value
This paper contributes to the existing literature on business intelligence analytics and continuous credit scoring by incorporating latent information of the relation network and external information from multi-source data (e.g. online app usage and offline consumption footprint). Also, the authors have proposed a power-law distribution-based network-aware decision support system to reinforce the prediction performance of individual telecom subscribers' credit scoring for the telecom marketing domain.
Keywords
Acknowledgements
This research was supported by the National Natural Science Foundation of China [grant number 71671048]; Guangdong Construction of High-Level Colleges for Postgraduate Study Abroad Project in Guangdong University of Technology [grant number 262515006]; and Top Innovation Graduate Student Cultivation Project Fund.
Citation
Gao, H., Liu, H., Ma, H., Ye, C. and Zhan, M. (2022), "Network-aware credit scoring system for telecom subscribers using machine learning and network analysis", Asia Pacific Journal of Marketing and Logistics, Vol. 34 No. 5, pp. 1010-1030. https://doi.org/10.1108/APJML-12-2020-0872
Publisher
:Emerald Publishing Limited
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