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

Ajay Byanjankar; Markku Heikkilä; Jozsef Mezei

doi:10.1109/SSCI.2015.109

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

Ajay Byanjankar, Markku Heikkilä, Jozsef Mezei

Research output: Chapter in Book/Conference proceeding › Conference contribution › Scientific › peer-review

120 Citations (Scopus)

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.

Original language	Undefined/Unknown
Title of host publication	2015 IEEE Symposium Series on Computational Intelligence: IEEE Symposium on Computational Intelligence for Financial Engineering & Economics
Editors	Andries Engelbrecht et al.
Publisher	IEEE
Pages	–
DOIs	https://doi.org/10.1109/SSCI.2015.109
Publication status	Published - 2015
MoE publication type	A4 Article in a conference publication
Event	conference; 2015-12-08; 2015-12-10 - 2015 IEEE Symposium Series on Computational Intelligence: IEEE Symposium on Computational Intelligence for Financial Engineering & Economics Duration: 8 Dec 2015 → 10 Dec 2015

Conference

Conference	conference; 2015-12-08; 2015-12-10
Period	08/12/15 → 10/12/15

Access to Document

10.1109/SSCI.2015.109

Cite this

@inproceedings{12a3b36559d3456399f40235458e3919,

title = "Predicting Credit Risk in Peer-to-Peer Lending: A Neural Network Approach",

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{\textquoteright} 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.",

author = "Ajay Byanjankar and Markku Heikkil{\"a} and Jozsef Mezei",

year = "2015",

doi = "10.1109/SSCI.2015.109",

language = "Odefinierat/ok{\"a}nt",

pages = "–",

editor = "{et al.}, {Andries Engelbrecht}",

booktitle = "2015 IEEE Symposium Series on Computational Intelligence: IEEE Symposium on Computational Intelligence for Financial Engineering & Economics",

publisher = "IEEE",

note = "conference; 2015-12-08; 2015-12-10 ; Conference date: 08-12-2015 Through 10-12-2015",

}

Byanjankar, A, Heikkilä, M & Mezei, J 2015, Predicting Credit Risk in Peer-to-Peer Lending: A Neural Network Approach. in AE et al. (ed.), 2015 IEEE Symposium Series on Computational Intelligence: IEEE Symposium on Computational Intelligence for Financial Engineering & Economics. IEEE, pp. –, conference; 2015-12-08; 2015-12-10, 08/12/15. https://doi.org/10.1109/SSCI.2015.109

Predicting Credit Risk in Peer-to-Peer Lending: A Neural Network Approach. / Byanjankar, Ajay; Heikkilä, Markku ; Mezei, Jozsef.
2015 IEEE Symposium Series on Computational Intelligence: IEEE Symposium on Computational Intelligence for Financial Engineering & Economics. ed. / Andries Engelbrecht et al. IEEE, 2015. p. –.

Research output: Chapter in Book/Conference proceeding › Conference contribution › Scientific › peer-review

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AB - 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.

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