Predicting Credit Risk in Peer-to-Peer Lending: A Neural Network Approach

A4 Conference proceedings


Internal Authors/Editors


Publication Details

List of Authors: Ajay Byanjankar, Markku Heikkilä, József Mezei
Editors: Andries Engelbrecht et al.
Place: Cape Town, South Africa
Publication year: 2015
Publisher: IEEE
Book title: 2015 IEEE Symposium Series on Computational Intelligence: IEEE Symposium on Computational Intelligence for Financial Engineering & Economics
Volume number: 8-10


Abstract

Emergence of peer-to-peer lending has opened an appealing option for micro-financing and is growing rapidly as an option in the financial industry. However, peer-to-peer lending possesses a high risk of investment failure due to the lack of expertise on the borrowers’ creditworthiness. In addition, information asymmetry, the unsecured nature of loans as well as lack of rigid rules and regulations increase the credit risk in peer-to-peer lending. This paper proposes a credit scoring model using artificial neural networks in classifying peer-to-peer loan applications into default and non-default groups. The results indicate that the neural network-based credit scoring model performs effectively in screening default applications.


Last updated on 2019-11-12 at 03:30