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

Forskningsoutput: Kapitel i bok/konferenshandlingKonferensbidragVetenskapligPeer review

53 Citeringar (Scopus)

Sammanfattning

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.
OriginalspråkOdefinierat/okänt
Titel på gästpublikation2015 IEEE Symposium Series on Computational Intelligence: IEEE Symposium on Computational Intelligence for Financial Engineering & Economics
RedaktörerAndries Engelbrecht et al.
FörlagIEEE
Sidor
DOI
StatusPublicerad - 2015
MoE-publikationstypA4 Artikel i en konferenspublikation
Evenemangconference; 2015-12-08; 2015-12-10 - 2015 IEEE Symposium Series on Computational Intelligence: IEEE Symposium on Computational Intelligence for Financial Engineering & Economics
Varaktighet: 8 dec 201510 dec 2015

Konferens

Konferensconference; 2015-12-08; 2015-12-10
Period08/12/1510/12/15

Citera det här