| Publikationstyp: | Beitrag in wissenschaftlicher Zeitschrift |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Latent factor models for credit scoring in P2P systems |
| Autor/-in: | Ahelegbey, Daniel Felix Giudici, Paolo Hadji Misheva, Branka |
| et. al: | No |
| DOI: | 10.1016/j.physa.2019.01.130 |
| Erschienen in: | Physica A: Statistical Mechanics and its Applications |
| Band(Heft): | 522 |
| Seite(n): | 112 |
| Seiten bis: | 121 |
| Erscheinungsdatum: | 2019 |
| Verlag / Hrsg. Institution: | Elsevier |
| ISSN: | 0378-4371 1873-2119 |
| Sprache: | Englisch |
| Schlagwörter: | Credit risk; Factor model; Financial technology; Scoring model; Spatial clustering; Peer-to-peer |
| Fachgebiet (DDC): | 332: Finanzwirtschaft |
| Zusammenfassung: | Peer-to-Peer (P2P) FinTech platforms allow cost reduction and service improvement in credit lending. However, these improvements may come at the price of a worse credit risk measurement, and this can hamper lenders and endanger the stability of a financial system. We approach the problem of credit risk for Peer-to-Peer (P2P) systems by presenting a latent factor-based classification technique to divide the population into major network communities in order to estimate a more efficient logistic model. Given a number of attributes that capture firm performances in a financial system, we adopt a latent position model which allow us to distinguish between communities of connected and not-connected firms based on the spatial position of the latent factors. We show through empirical illustration that incorporating the latent factor-based classification of firms is particularly suitable as it improves the predictive performance of P2P scoring models. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/23448 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Datenanalyse und Prozessdesign (IDP) |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
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Ahelegbey, D. F., Giudici, P., & Hadji Misheva, B. (2019). Latent factor models for credit scoring in P2P systems. Physica A: Statistical Mechanics and Its Applications, 522, 112–121. https://doi.org/10.1016/j.physa.2019.01.130
Ahelegbey, D.F., Giudici, P. and Hadji Misheva, B. (2019) ‘Latent factor models for credit scoring in P2P systems’, Physica A: Statistical Mechanics and its Applications, 522, pp. 112–121. Available at: https://doi.org/10.1016/j.physa.2019.01.130.
D. F. Ahelegbey, P. Giudici, and B. Hadji Misheva, “Latent factor models for credit scoring in P2P systems,” Physica A: Statistical Mechanics and its Applications, vol. 522, pp. 112–121, 2019, doi: 10.1016/j.physa.2019.01.130.
AHELEGBEY, Daniel Felix, Paolo GIUDICI und Branka HADJI MISHEVA, 2019. Latent factor models for credit scoring in P2P systems. Physica A: Statistical Mechanics and its Applications. 2019. Bd. 522, S. 112–121. DOI 10.1016/j.physa.2019.01.130
Ahelegbey, Daniel Felix, Paolo Giudici, and Branka Hadji Misheva. 2019. “Latent Factor Models for Credit Scoring in P2P Systems.” Physica A: Statistical Mechanics and Its Applications 522: 112–21. https://doi.org/10.1016/j.physa.2019.01.130.
Ahelegbey, Daniel Felix, et al. “Latent Factor Models for Credit Scoring in P2P Systems.” Physica A: Statistical Mechanics and Its Applications, vol. 522, 2019, pp. 112–21, https://doi.org/10.1016/j.physa.2019.01.130.
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