|Publication type:||Article in scientific journal|
|Type of review:||Peer review (publication)|
|Title:||Latent factor models for credit scoring in P2P systems|
|Authors:||Ahelegbey, Daniel Felix|
Hadji Misheva, Branka
|Published in:||Physica A: Statistical Mechanics and its Applications|
|Publisher / Ed. Institution:||Elsevier|
|Subjects:||Credit risk; Factor model; Financial technology; Scoring model; Spatial clustering; Peer-to-peer|
|Subject (DDC):||332: Financial economics|
|Abstract:||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.|
|Fulltext version:||Published version|
|License (according to publishing contract):||Licence according to publishing contract|
|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). 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, 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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