Credit Risk Assessment Based on Flexible Neural Tree Model

被引：4

作者：

Zhang, Yishen ^{[1
,2
]}

Wang, Dong ^{[1
,2
]}

Chen, Yuehui ^{[1
,2
]}

Zhao, Yaou ^{[1
,2
]}

Shao, Peng ^{[3
]}

Meng, Qingfang ^{[1
,2
]}

机构：

[1] Univ Jinan, Sch Informat Sci & Engn, Jinan, Shandong, Peoples R China

[2] Shandong Prov Key Lab Network Based Intelligent C, Jinan 250022, Shandong, Peoples R China

[3] Dalian Univ Technol, Sch Math, Dalian, Peoples R China

来源：

ADVANCES IN NEURAL NETWORKS, PT I | 2017年 / 10261卷

基金：

中国国家自然科学基金;

关键词：

Artificial neural network; Credit risk assessment; Flexible neural tree; NETWORKS;

D O I：

10.1007/978-3-319-59072-1_26

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In recent years, as China's credit market continues to expand, a large number of P2P (person-to-person borrow or lend money in Internet Finance) platforms were born and developed. Most of the P2P platforms in China use data mining methods to evaluate the credit risk of loan applicants. Artificial neural network (ANN) is an emerging data mining tool and has good classification ability in many application fields. This paper presents a model of credit risk assessment based on flexible neural tree (FNT), which can reduce the overdue rate and save the analysis time. Overdue and non-overdue sample data are provided by the Jinan Hengxin Micro-Investment Advisory Co., Ltd., and used to build the model. Experiments show that the proposed model is more accurate and has less time cost for the overdue classification of credit risk assessment.

引用

页码：215 / 222

页数：8

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