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https://doi.org/10.21256/zhaw-19483| Publikationstyp: | Beitrag in wissenschaftlicher Zeitschrift |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Network based scoring models to improve credit risk management in peer to peer lending platforms |
| Autor/-in: | Giudici, Paolo Hadji Misheva, Branka Spelta, Alessandro |
| et. al: | No |
| DOI: | 10.3389/frai.2019.00003 10.21256/zhaw-19483 |
| Erschienen in: | Frontiers in Artificial Intelligence |
| Band(Heft): | 2 |
| Heft: | 3 |
| Erscheinungsdatum: | 2019 |
| Verlag / Hrsg. Institution: | Frontiers Research Foundation |
| ISSN: | 2624-8212 |
| Sprache: | Englisch |
| Schlagwörter: | Contagion; Credit risk; Credit scoring; Network model; Peer to peer lending |
| Fachgebiet (DDC): | 004: Informatik 332: Finanzwirtschaft |
| Zusammenfassung: | 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 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | CC BY 4.0: Namensnennung 4.0 International |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Datenanalyse und Prozessdesign (IDP) |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2019_Hadji-Misheva_Credit-Risk-Management.pdf | 1.45 MB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
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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