%0 Journal Article
%T 基于网络信贷平台的客户信息挖掘<br>Customer Information Mining Based on Internet Credit Platform
%A 史佳璐
%A 向永宏
%A 芦俊丽
%J Hans Journal of Data Mining
%P 167-180
%@ 2163-1468
%D 2021
%I Hans Publishing
%R 10.12677/HJDM.2021.113015
%X 随着我国网络支付的迅速发展，网络信贷平台在日常生活中初露头角，然而优质客源对网络信贷平台的发展至关重要。本文对某网络信贷平台的数据进行了分析和挖掘。首先，从客户贷款特征数据中对客户价值进行聚类分析，并针对可发展客户给出相应决策。其次，根据客户的借款类型数据，对客户等级进行重新评价，指明提高平台用户质量、数量的方向。最后，利用已认证客户的信息，研究已认证客户喜好的认证方式与客户属性的关系，按客户喜好推荐认证方式以提高客户的认证率，从而增加客户源的稳定性。综上，本文从三个方面进行了研究，对网络信贷平台的发展具有重要意义。<br />
With the rapid development of online payment in China, online credit platforms are emerging in daily life. However, high-quality customer sources are crucial to the development of online credit platforms. This article analyzes and mines the data of an online credit platform. Firstly, the cluster-ing analysis of customer value is carried out from customer loan characteristic data, and the corre-sponding decision is given for the developing customer. Secondly, according to the type of loan data of customers, the level of customers is reevaluated, and the direction of improving the quality and quantity of platform users is pointed out. Finally, using the information of the certified customers, the relationship between the preferred authentication methods of the certified customers and the attributes of the customers is studied, and the authentication methods are recommended according to the preferences of the customers to improve the certification rate of the customers, so as to in-crease the stability of the customer source. The objective is to increase the customer’s authentica-tion rate and the stability of the customer source. In summary, this article conducts research in three aspects above, which is significant to the development of online credit platforms.
%K 网络信贷平台，客户价值聚类，客户分群，认证方式推荐<br>Online Credit Platform
%K Customer Value Clustering
%K Customer Grouping
%K Authentication Mode Rec-ommendation
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=43595