Bitte benutzen Sie diese Kennung, um auf die Ressource zu verweisen:
https://doi.org/10.21256/zhaw-23433| Publikationstyp: | Beitrag in wissenschaftlicher Zeitschrift |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Factorial network models to improve P2P credit risk management |
| Autor/-in: | Ahelegbey, Daniel Felix Giudici, Paolo Hadji Misheva, Branka |
| et. al: | No |
| DOI: | 10.3389/frai.2019.00008 10.21256/zhaw-23433 |
| Erschienen in: | Frontiers in Artificial Intelligence |
| Band(Heft): | 2 |
| Seite(n): | 8 |
| Erscheinungsdatum: | 2019 |
| Verlag / Hrsg. Institution: | Frontiers Research Foundation |
| ISSN: | 2624-8212 |
| Sprache: | Englisch |
| Schlagwörter: | FinTech; Credit risk; Credit scoring; Factor models; Lasso; Peer-to-peer lending; Segmentation |
| Fachgebiet (DDC): | 332: Finanzwirtschaft |
| Zusammenfassung: | This paper investigates how to improve statistical-based credit scoring of SMEs involved in P2P lending. The methodology discussed in the paper is a factor network-based segmentation for credit score modeling. The approach first constructs a network of SMEs where links emerge from comovement of latent factors, which allows us to segment the heterogeneous population into clusters. We then build a credit score model for each cluster via lasso-type regularization logistic regression. We compare our approach with the conventional logistic model by analyzing the credit score of over 1,5000 SMEs engaged in P2P lending services across Europe. The result reveals that credit risk modeling using our network-based segmentation achieves higher predictive performance than the conventional model. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/23433 |
| 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_Ahelegbey_Factorial_network_models_to_improve_P2P_credit_risk_management_Frontiers_in_Artificial_Intelligence.pdf | 695.61 kB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
Ahelegbey, D. F., Giudici, P., & Hadji Misheva, B. (2019). Factorial network models to improve P2P credit risk management. Frontiers in Artificial Intelligence, 2, 8. https://doi.org/10.3389/frai.2019.00008
Ahelegbey, D.F., Giudici, P. and Hadji Misheva, B. (2019) ‘Factorial network models to improve P2P credit risk management’, Frontiers in Artificial Intelligence, 2, p. 8. Available at: https://doi.org/10.3389/frai.2019.00008.
D. F. Ahelegbey, P. Giudici, and B. Hadji Misheva, “Factorial network models to improve P2P credit risk management,” Frontiers in Artificial Intelligence, vol. 2, p. 8, 2019, doi: 10.3389/frai.2019.00008.
AHELEGBEY, Daniel Felix, Paolo GIUDICI und Branka HADJI MISHEVA, 2019. Factorial network models to improve P2P credit risk management. Frontiers in Artificial Intelligence. 2019. Bd. 2, S. 8. DOI 10.3389/frai.2019.00008
Ahelegbey, Daniel Felix, Paolo Giudici, and Branka Hadji Misheva. 2019. “Factorial Network Models to Improve P2P Credit Risk Management.” Frontiers in Artificial Intelligence 2: 8. https://doi.org/10.3389/frai.2019.00008.
Ahelegbey, Daniel Felix, et al. “Factorial Network Models to Improve P2P Credit Risk Management.” Frontiers in Artificial Intelligence, vol. 2, 2019, p. 8, https://doi.org/10.3389/frai.2019.00008.
Alle Ressourcen in diesem Repository sind urheberrechtlich geschützt, soweit nicht anderweitig angezeigt.