Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-23432
Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Network based credit risk models
Authors: Giudici, Paolo
Hadji Misheva, Branka
Spelta, Alessandro
et. al: No
DOI: 10.1080/08982112.2019.1655159
10.21256/zhaw-23432
Published in: Quality Engineering
Volume(Issue): 32
Issue: 2
Page(s): 199
Pages to: 211
Issue Date: 2019
Publisher / Ed. Institution: Taylor & Francis
ISSN: 0898-2112
1532-4222
Language: English
Subjects: Credit scoring model; Network model; Peer-to-peer lending
Subject (DDC): 332: Financial economics
Abstract: Peer-to-Peer lending platforms may lead to cost reduction, and to an improved user experience. These improvements may come at the price of inaccurate credit risk measurements, which can hamper lenders and endanger the stability of a financial system. In the article, we propose how to improve credit risk accuracy of peer to peer platforms and, specifically, of those who lend to small and medium enterprises. To achieve this goal, we propose toaugment traditional credit scoring methods with “alternative data” that consist of centralitymeasures derived from similarity networks among borrowers, deduced from their financialratios. Our empirical findings suggest that the proposed approach improves predictiveaccuracy as well as model explainability.
URI: https://digitalcollection.zhaw.ch/handle/11475/23432
Fulltext version: Published version
License (according to publishing contract): CC BY-NC-ND 4.0: Attribution - Non commercial - No derivatives 4.0 International
Departement: School of Engineering
Organisational Unit: Institute of Data Analysis and Process Design (IDP)
Appears in collections:Publikationen School of Engineering

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