Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-23433
Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Factorial network models to improve P2P credit risk management
Authors: Ahelegbey, Daniel Felix
Giudici, Paolo
Hadji Misheva, Branka
et. al: No
DOI: 10.3389/frai.2019.00008
10.21256/zhaw-23433
Published in: Frontiers in Artificial Intelligence
Volume(Issue): 2
Page(s): 8
Issue Date: 2019
Publisher / Ed. Institution: Frontiers Research Foundation
ISSN: 2624-8212
Language: English
Subjects: FinTech; Credit risk; Credit scoring; Factor models; Lasso; Peer-to-peer lending; Segmentation
Subject (DDC): 332: Financial economics
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.
URI: https://digitalcollection.zhaw.ch/handle/11475/23433
Fulltext version: Published version
License (according to publishing contract): CC BY 4.0: Attribution 4.0 International
Departement: School of Engineering
Organisational Unit: Institute of Data Analysis and Process Design (IDP)
Appears in collections:Publikationen School of Engineering



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