Latent factor models for credit scoring in P2P systems

Daniel Felix Ahelegbey, Paolo Giudici and Branka Hadji-Misheva

Physica A: Statistical Mechanics and its Applications, 2019, vol. 522, issue C, 112-121

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.

Keywords: Credit risk; Factor models; Financial technology; Peer-to-peer; Scoring models; Spatial clustering (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Working Paper: Latent Factor Models for Credit Scoring in P2P Systems (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:522:y:2019:i:c:p:112-121

DOI: 10.1016/j.physa.2019.01.130

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