Factorial Network Models to Improve P2P Credit Risk Management

Ahelegbey, Daniel Felix; Giudici, Paolo; Hadji Misheva, Branka (2019). Factorial Network Models to Improve P2P Credit Risk Management Frontiers in Artificial Intelligence, 2, pp. 1-9. Frontiers Research Foundation 10.3389/frai.2019.00008

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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.

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:	2624-8212
Publisher:	Frontiers Research Foundation
Language:	English
Submitter:	Branka Hadji Misheva
Date Deposited:	17 Aug 2022 11:11
Last Modified:	17 Aug 2022 11:11
Publisher DOI:	10.3389/frai.2019.00008
Uncontrolled Keywords:	credit risk, factor models, FinTech, peer-to-peer lending, credit scoring, lasso, segmentation
ARBOR DOI:	10.24451/arbor.17302
URI:	https://arbor.bfh.ch/id/eprint/17302