Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Latent factor models for credit scoring in P2P systems
Authors: Ahelegbey, Daniel Felix
Giudici, Paolo
Hadji Misheva, Branka
et. al: No
DOI: 10.1016/j.physa.2019.01.130
Published in: Physica A: Statistical Mechanics and its Applications
Volume(Issue): 522
Page(s): 112
Pages to: 121
Issue Date: 2019
Publisher / Ed. Institution: Elsevier
ISSN: 0378-4371
Language: English
Subjects: Credit risk; Factor model; Financial technology; Scoring model; Spatial clustering; Peer-to-peer
Subject (DDC): 332: Financial economics
Abstract: Peer-to-Peer (P2P) FinTech platforms allow cost reduction and service improvement in credit lending. However, these improvements may come at the price of a worse credit risk measurement, and this can hamper lenders and endanger the stability of a financial system. We approach the problem of credit risk for Peer-to-Peer (P2P) systems by presenting a latent factor-based classification technique to divide the population into major network communities in order to estimate a more efficient logistic model. Given a number of attributes that capture firm performances in a financial system, we adopt a latent position model which allow us to distinguish between communities of connected and not-connected firms based on the spatial position of the latent factors. We show through empirical illustration that incorporating the latent factor-based classification of firms is particularly suitable as it improves the predictive performance of P2P scoring models.
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
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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