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Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Network based scoring models to improve credit risk management in peer to peer lending platforms
Authors: Giudici, Paolo
Hadji Misheva, Branka
Spelta, Alessandro
et. al: No
DOI: 10.3389/frai.2019.00003
Published in: Frontiers in Artificial Intelligence
Volume(Issue): 2
Issue: 3
Issue Date: 2019
Publisher / Ed. Institution: Frontiers Research Foundation
ISSN: 2624-8212
Language: English
Subjects: Contagion; Credit risk; Credit scoring; Network model; Peer to peer lending
Subject (DDC): 004: Computer science
332: Financial economics
Abstract: Financial intermediation has changed extensively over the course of the last two decades. One of the most significant change has been the emergence of FinTech. In the context of credit services, fintech peer to peer lenders have introduced many opportunities, among which improved speed, better customer experience, and reduced costs. However, peer-to-peer lending platforms lead to higher risks, among which higher credit risk: not owned by the lenders, and systemic risks: due to the high interconnectedness among borrowers generated by the platform. This calls for new and more accurate credit risk models to protect consumers and preserve financial stability. In this paper we propose to enhance credit risk accuracy of peer-to-peer platforms by leveraging topological information embedded into similarity networks, derived from borrowers' financial information. Topological coefficients describing borrowers' importance and community structures are employed as additional explanatory variables, leading to an improved predictive performance of credit scoring models.
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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