Latent factor models for credit scoring in P2P systems

Ahelegbey, Daniel Felix; Giudici, Paolo; Hadji Misheva, Branka

doi:10.1016/j.physa.2019.01.130

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 Giudici, Paolo Hadji Misheva, Branka
et. al:	No
DOI:	10.1016/j.physa.2019.01.130
Published in:	Physica A: Statistical Mechanics and its Applications
Volume(Issue):	522
Page(s):	112
Pages to:	121
Issue Date:	2019
Publisher / Ed. Institution:	Elsevier
ISSN:	0378-4371 1873-2119
Language:	English
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.
URI:	https://mpra.ub.uni-muenchen.de/id/eprint/92636 https://digitalcollection.zhaw.ch/handle/11475/23448
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, Paolo GIUDICI und Branka HADJI MISHEVA, 2019. Latent factor models for credit scoring in P2P systems. Physica A: Statistical Mechanics and its Applications [online]. 2019. Bd. 522, S. 112–121. DOI 10.1016/j.physa.2019.01.130. Verfügbar unter: https://mpra.ub.uni-muenchen.de/id/eprint/92636

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.