Latent factor models for credit scoring in P2P systems

Ahelegbey, Daniel Felix; Giudici, Paolo; Hadji Misheva, Branka (2019). Latent factor models for credit scoring in P2P systems Physica A: Statistical Mechanics and its Applications, 522, pp. 112-121. Elsevier 10.1016/j.physa.2019.01.130

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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.

Item Type:	Journal Article (Original Article)
Division/Institute:	Business School > Institute for Applied Data Science & Finance Business School
Name:	Ahelegbey, Daniel Felix; Giudici, Paolo and Hadji Misheva, Branka
Subjects:	H Social Sciences > HG Finance
ISSN:	03784371
Publisher:	Elsevier
Language:	English
Submitter:	Branka Hadji Misheva
Date Deposited:	17 Aug 2022 11:10
Last Modified:	17 Aug 2022 11:10
Publisher DOI:	10.1016/j.physa.2019.01.130
Uncontrolled Keywords:	Credit risk, Factor models, Financial technology, Peer-to-peer, Scoring models, Spatial clustering
ARBOR DOI:	10.24451/arbor.17301
URI:	https://arbor.bfh.ch/id/eprint/17301