New Methods of Customer Segmentation and Individual Credit Evaluation Based on Machine Learning

DOI

10.2991/aebmr.k.200324.170How to use a DOI?

Keywords

customer segmentation, digital payments, BP neural network, machine learning, personal credit score

Abstract

The internet has enabled a fundamental change in consumer behaviour and their understanding of e-commerce business. The main objective of the following article is to present the latest trends in the way of client segmentation associated with individual credit evaluation based on machine learning. The first part discusses the current situation and innovations in the way people pay in an omnichannel world. We describe how the absence of physical money has affected society, how it has changed customer purchasing behaviour, and what this change means for the digital economy and marketing. In the background of the rapid development of big data and the Internet technology, the traditional personal credit evaluation method of the commercial bank faces a significant challenge in the evaluation of personal credit. Based on the limitation of the existing personal credit evaluation method, the second part discusses the necessity of the research on the personal credit evaluation based on the machine learning method and then probes into the comprehensive personal credit evaluation dimension and the advanced data acquisition method of the Internet finance company. And then, the data desensitisation and LOF test were carried out by dynamic desensitisation technique. The abnormal value of the tested data and the random forest method supplement the missing value of the data. The importance index is screened by the gradient boosting decision tree method, and the personal credit evaluation score is output through the scorecard model based on logical regression. After that, the model is tested by BP neural network, and the personal credit level is predicted. The personal credit level fosters customer market segmentation.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zhou Yuping
AU  - Petra Jílková
AU  - Chen Guanyu
AU  - David Weisl
PY  - 2020
DA  - 2020/03/30
TI  - New Methods of Customer Segmentation and Individual Credit Evaluation Based on Machine Learning
BT  - Proceedings of the “New Silk Road: Business Cooperation and Prospective of Economic Development” (NSRBCPED 2019)
PB  - Atlantis Press
SP  - 925
EP  - 931
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.200324.170
DO  - 10.2991/aebmr.k.200324.170
ID  - Yuping2020
ER  -