文章基本信息

标题：Credit Risk Classification in Peer-to-Peer Marketplaces The Nexus of Neural Network Approach
本地全文：下载
作者：Baah Alexander ; Tan Zhongming ; Ding Guoping 等
期刊名称：Business and Economic Research
印刷版ISSN：2162-4860
出版年度：2020
卷号：10
期号：1
页码：196-209
DOI：10.5296/ber.v10i1.16372
语种：English
出版社：Macrothink Institute, Inc.
摘要：Financial innovation in recent years have prominently contributed to the growth of Peer-to-Peer lending marketplaces allowing individual and businesses to secure loans on a common internet-based network. Similar to the ‘bricks and mortar’ banking system, online lending is coupled with the problem of information asymmetry. Borrower risk assessment has henceforth become the major concerns of various platforms that aim to reducing information imbalance towards mitigating credit risk. In this article, authors compared two learning algorithms – Logistic regression and Artificial Neural Network to classify borrowers based on loan repayment schedule. We revealed that both approaches were robust in classifying late borrowers with logistic regression being 0.02% more robust than Neural Network. Regarding variable relative importance, gender is considered the least important variable whereas terms-of-repayment is the most important variable affecting borrowers’ intention to pay off loans. Even though our study contributes to existing literature, it is however not limited to determining factors that may affect lenders’ investment decision in social lending.
关键词：Artificial Neural Network; Financial innovation; Information asymmetry;Algorithm