Economics at your fingertips  

Factorial Network Models To Improve P2P Credit Risk Management

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

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Credit Risk; Factor models; Fintech; Peer-to-Peer lending; Credit Scoring; Lasso; Segmentation (search for similar items in EconPapers)
JEL-codes: C38 G2 (search for similar items in EconPapers)
Date: 2019-02-26
New Economics Papers: this item is included in nep-ent, nep-pay, nep-rmg and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link) original version (application/pdf) revised version (application/pdf) revised version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().

Page updated 2020-07-08
Handle: RePEc:pra:mprapa:92633