Risk assessment in social lending via random forests

被引：295

作者：

Malekipirbazari, Milad ^{[1
]}

Aksakalli, Vural ^{[1
]}

机构：

[1] Istanbul Sehir Univ, TR-34662 Istanbul, Turkey

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2015年 / 42卷 / 10期

关键词：

Peer-to-peer lending; Social lending; Risk assessment; Machine learning; Random forest; SUPPORT VECTOR MACHINES; MODEL;

D O I：

10.1016/j.eswa.2015.02.001

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

With the advance of electronic commerce and social platforms, social lending (also known as peer-to-peer lending) has emerged as a viable platform where lenders and borrowers can do business without the help of institutional intermediaries such as banks. Social lending has gained significant momentum recently, with some platforms reaching multi-billion dollar loan circulation in a short amount of time. On the other hand, sustainability and possible widespread adoption of such platforms depend heavily on reliable risk attribution to individual borrowers. For this purpose, we propose a random forest (RF) based classification method for predicting borrower status. Our results on data from the popular social lending platform Lending Club (LC) indicate the RF-based method outperforms the FICO credit scores as well as LC grades in identification of good borrowers. (C) 2015 Elsevier Ltd. All rights reserved.

引用

页码：4621 / 4631

页数：11

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