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Latent Factor Models for Credit Scoring in P2P Systems

Daniel Felix Ahelegbey (), Paolo Giudici () and Branka Hadji-Misheva

MPRA Paper from University Library of Munich, Germany

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)
JEL-codes: C38 G10 G21 (search for similar items in EconPapers)
Date: 2018-07-04, Revised 2018-10-11
New Economics Papers: this item is included in nep-pay and nep-ure
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Forthcoming in Physica A: Statistical Mechanics and its Applications 522 (2019): pp. 112-121

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Journal Article: Latent factor models for credit scoring in P2P systems (2019) Downloads
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