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https://doi.org/10.21256/zhaw-19483| Publication type: | Article in scientific journal |
| Type of review: | Peer review (publication) |
| Title: | Network based scoring models to improve credit risk management in peer to peer lending platforms |
| Authors: | Giudici, Paolo Hadji Misheva, Branka Spelta, Alessandro |
| et. al: | No |
| DOI: | 10.3389/frai.2019.00003 10.21256/zhaw-19483 |
| Published in: | Frontiers in Artificial Intelligence |
| Volume(Issue): | 2 |
| Issue: | 3 |
| Issue Date: | 2019 |
| Publisher / Ed. Institution: | Frontiers Research Foundation |
| ISSN: | 2624-8212 |
| Language: | English |
| Subjects: | Contagion; Credit risk; Credit scoring; Network model; Peer to peer lending |
| Subject (DDC): | 004: Computer science 332: Financial economics |
| Abstract: | 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/19483 |
| Fulltext version: | Published version |
| License (according to publishing contract): | CC BY 4.0: Attribution 4.0 International |
| Departement: | School of Engineering |
| Organisational Unit: | Institute of Data Analysis and Process Design (IDP) |
| Appears in collections: | Publikationen School of Engineering |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2019_Hadji-Misheva_Credit-Risk-Management.pdf | 1.45 MB | Adobe PDF |  View/Open |
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Giudici, P., Hadji Misheva, B., & Spelta, A. (2019). Network based scoring models to improve credit risk management in peer to peer lending platforms. Frontiers in Artificial Intelligence, 2(3). https://doi.org/10.3389/frai.2019.00003
Giudici, P., Hadji Misheva, B. and Spelta, A. (2019) ‘Network based scoring models to improve credit risk management in peer to peer lending platforms’, Frontiers in Artificial Intelligence, 2(3). Available at: https://doi.org/10.3389/frai.2019.00003.
P. Giudici, B. Hadji Misheva, and A. Spelta, “Network based scoring models to improve credit risk management in peer to peer lending platforms,” Frontiers in Artificial Intelligence, vol. 2, no. 3, 2019, doi: 10.3389/frai.2019.00003.
GIUDICI, Paolo, Branka HADJI MISHEVA und Alessandro SPELTA, 2019. Network based scoring models to improve credit risk management in peer to peer lending platforms. Frontiers in Artificial Intelligence. 2019. Bd. 2, Nr. 3. DOI 10.3389/frai.2019.00003
Giudici, Paolo, Branka Hadji Misheva, and Alessandro Spelta. 2019. “Network Based Scoring Models to Improve Credit Risk Management in Peer to Peer Lending Platforms.” Frontiers in Artificial Intelligence 2 (3). https://doi.org/10.3389/frai.2019.00003.
Giudici, Paolo, et al. “Network Based Scoring Models to Improve Credit Risk Management in Peer to Peer Lending Platforms.” Frontiers in Artificial Intelligence, vol. 2, no. 3, 2019, https://doi.org/10.3389/frai.2019.00003.
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