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

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussa › Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

111 Sitaatiot (Scopus)

Abstrakti

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.

Alkuperäiskieli	Ei tiedossa
Otsikko	2015 IEEE Symposium Series on Computational Intelligence: IEEE Symposium on Computational Intelligence for Financial Engineering & Economics
Toimittajat	Andries Engelbrecht et al.
Kustantaja	IEEE
Sivut	–
DOI - pysyväislinkit	https://doi.org/10.1109/SSCI.2015.109
Tila	Julkaistu - 2015
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisuussa
Tapahtuma	conference; 2015-12-08; 2015-12-10 - 2015 IEEE Symposium Series on Computational Intelligence: IEEE Symposium on Computational Intelligence for Financial Engineering & Economics Kesto: 8 jouluk. 2015 → 10 jouluk. 2015

Konferenssi

Konferenssi	conference; 2015-12-08; 2015-12-10
Ajanjakso	08/12/15 → 10/12/15

Pääsy asiakirjaan

10.1109/SSCI.2015.109

Viittausmuodot

@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. julkaisussa AE et al. (Toimittaja), 2015 IEEE Symposium Series on Computational Intelligence: IEEE Symposium on Computational Intelligence for Financial Engineering & Economics. IEEE, Sivut –, 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. toim. / Andries Engelbrecht et al. IEEE, 2015. s. –.

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussa › Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

TY - GEN

T1 - Predicting Credit Risk in Peer-to-Peer Lending: A Neural Network Approach

AU - Byanjankar, Ajay

AU - Heikkilä, Markku

AU - Mezei, Jozsef

PY - 2015

Y1 - 2015

N2 - 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.

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.

U2 - 10.1109/SSCI.2015.109

DO - 10.1109/SSCI.2015.109

M3 - Konferensbidrag

SP - –

BT - 2015 IEEE Symposium Series on Computational Intelligence: IEEE Symposium on Computational Intelligence for Financial Engineering & Economics

A2 - et al., Andries Engelbrecht

PB - IEEE

T2 - conference; 2015-12-08; 2015-12-10

Y2 - 8 December 2015 through 10 December 2015

ER -