Can small sample dataset be used for efficient internet loan credit risk assessment? Evidence from online peer to peer lending

被引：19
|
作者：
Yu, Lean [1 ,2 ]
Zhang, Xiaoming [1 ]
机构：
[1] Harbin Engn Univ, Sch Econ & Management, Harbin 150001, Peoples R China
[2] Beijing Univ Chem Technol, Sch Econ & Management, Beijing 100029, Peoples R China
来源：
FINANCE RESEARCH LETTERS | 2021年 / 38卷
基金：
中国国家自然科学基金;
关键词：
Peer to peer lending; Small sample; Bootstrapping; mega-trend-diffusion; Particle swarm optimization; Virtual sample generation; Internet loan credit risk evaluation;
D O I：
10.1016/j.frl.2020.101521
中图分类号：
F8 [财政、金融];
学科分类号：
0202 ;
摘要：
The emerging online peer to peer (P2P) lending platforms have only a small number of samples in the early stage, it is thus unable to conduct an efficient credit risk assessment on internet loan applicants. In order to solve the sample shortage issue, a virtual sample generation (VSG) methodology integrating multi-distribution mega-trend-diffusion (MD-MTD) and particle swarm optimization (PSO) algorithm is proposed for internet loan credit risk evaluation with small samples. The empirical results indicate that the proposed VSG methodology can greatly help to improve performance of the internet loan credit risk evaluation with small sample datasets.
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收藏
页数：7
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