Factorial Network Models To Improve P2P Credit Risk Management

Abstract

This paper investigates how to improve statistical-based credit scoring of SMEs involved in P2P lending. The methodology discussed in the paper is a factor network-based segmentation for credit score modeling. The approach first constructs a network of SMEs where links emerge from comovement of latent factors, which allows us to segment the heterogeneous population into clusters. We then build a credit score model for each cluster via lasso logistic regression. We compare our approach with the conventional logistic model by analyzing the credit score of over 15000 SMEs engaged in P2P lending services across Europe. The result reveals that credit risk modeling using our network-based segmentation achieves higher predictive performance than the conventional model.

Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets

“…The peer-to-peer (or person-to-person, P2P) lending market facilitates financial transactions between borrowers and lenders. Considering that services are processed through Internet technologies, P2P lending is considered a financial innovation, a socalled FinTech product (Ahelegbey et al 2019;Kim and Cho 2019b;Kou et al 2021a;Allen et al 2021; for a broader review of FinTech-related research). The P2P lending market offers the possibility for borrowers who would often not be eligible for a loan through bank-offered services to obtain one (e.g., Li et al 2018).…”