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-type regularization logistic regression. We compare our approach with the conventional logistic model by analyzing the credit score of over 1,5000 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.

P2P lending platforms in Malaysia: What do we know?

“…Some P2P lending platforms also apply profit scoring approaches since they consider minimising borrowers' defaults and the intention to maximise lenders' profits (Serrano-Cinca & Gutiérrez-Nieto, 2016;Ye, Dong & Ma, 2018). However, such general risk credit scoring methods may be inadequate in providing an accurate probability of default since borrowers from different geographical areas may behave differently (Ahelegbey, Giudici & Hadji-Misheva, 2018). Thus, moral hazard may be a challenging issue that may keep investors away from the P2P lending industry (Suryono, Budi & Purwandari, 2020).…”

Impact of Credit Risk Management on the Financial Performance of Microfinance Institutions in Nigeria: A Qualitative Review

“…The stability, viability, and sustainability of the MFIs are at risk due to this type of drift. Credit management, or more precisely credit risk management, refers to the strategies, procedures, and controls that a company uses to ensure efficient client payment collection and so lower the risk of non-payment (Ahelegbey et al, 2019).…”

P2P lending platforms in Malaysia: What do we know?

“…Some P2P lending platforms also apply profit scoring approaches since they consider minimising borrowers’ defaults and the intention to maximise lenders’ profits ( Serrano-Cinca & Gutiérrez-Nieto, 2016 ; Ye, Dong & Ma, 2018 ). However, such general risk credit scoring methods may be inadequate in providing an accurate probability of default since borrowers from different geographical areas may behave differently ( Ahelegbey, Giudici & Hadji-Misheva, 2018 ). Thus, moral hazard may be a challenging issue that may keep investors away from the P2P lending industry ( Suryono, Budi & Purwandari, 2020 ).…”