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https://doi.org/10.21256/zhaw-23433| Publication type: | Article in scientific journal |
| Type of review: | Peer review (publication) |
| Title: | Factorial network models to improve P2P credit risk management |
| Authors: | Ahelegbey, Daniel Felix Giudici, Paolo Hadji Misheva, Branka |
| et. al: | No |
| DOI: | 10.3389/frai.2019.00008 10.21256/zhaw-23433 |
| Published in: | Frontiers in Artificial Intelligence |
| Volume(Issue): | 2 |
| Page(s): | 8 |
| Issue Date: | 2019 |
| Publisher / Ed. Institution: | Frontiers Research Foundation |
| ISSN: | 2624-8212 |
| Language: | English |
| Subjects: | FinTech; Credit risk; Credit scoring; Factor models; Lasso; Peer-to-peer lending; Segmentation |
| Subject (DDC): | 332: Financial economics |
| Abstract: | 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 |
| 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 |
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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.
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