Giudici, Paolo; Hadji Misheva, Branka; Spelta, Alessandro (2019). Network Based Scoring Models to Improve Credit Risk Management in Peer to Peer Lending Platforms Frontiers in Artificial Intelligence, 2, pp. 1-8. Frontiers Research Foundation 10.3389/frai.2019.00003
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Financial intermediation has changed extensively over the course of the last two decades. One of the most significant change has been the emergence of FinTech. In the context of credit services, fintech peer to peer lenders have introduced many opportunities, among which improved speed, better customer experience, and reduced costs. However, peer-to-peer lending platforms lead to higher risks, among which higher credit risk: not owned by the lenders, and systemic risks: due to the high interconnectedness among borrowers generated by the platform. This calls for new and more accurate credit risk models to protect consumers and preserve financial stability. In this paper we propose to enhance credit risk accuracy of peer-to-peer platforms by leveraging topological information embedded into similarity networks, derived from borrowers' financial information. Topological coefficients describing borrowers' importance and community structures are employed as additional explanatory variables, leading to an improved predictive performance of credit scoring models.
Item Type: |
Journal Article (Original Article) |
|---|---|
Division/Institute: |
Business School > Institute for Applied Data Science & Finance Business School |
Name: |
Giudici, Paolo; Hadji Misheva, Branka and Spelta, Alessandro |
Subjects: |
H Social Sciences > HG Finance |
ISSN: |
2624-8212 |
Publisher: |
Frontiers Research Foundation |
Language: |
English |
Submitter: |
Branka Hadji Misheva |
Date Deposited: |
17 Aug 2022 11:04 |
Last Modified: |
07 Sep 2022 11:13 |
Publisher DOI: |
10.3389/frai.2019.00003 |
Uncontrolled Keywords: |
contagion, credit risk, credit scoring, network models, peer to peer lending |
ARBOR DOI: |
10.24451/arbor.17298 |
URI: |
https://arbor.bfh.ch/id/eprint/17298 |
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