Credit Risk Evaluation in Peer-to-peer Lending With Linguistic Data Transformation and Supervised Learning

dc.contributor.author Mezei, Jozsef
dc.contributor.author Byanjankar, Ajay
dc.contributor.author Heikkilä, Markku
dc.date.accessioned 2017-12-28T00:47:56Z
dc.date.available 2017-12-28T00:47:56Z
dc.date.issued 2018-01-03
dc.description.abstract The widespread availability of various peer-to-peer lending solutions is rapidly changing the landscape of ï¬ nancial services. Beside the natural advantages over traditional services,a relevant problem in the domain is to correctly assess the risk associated with borrowers. In contrast to traditional ï¬ nancial services industries, in peer-to-peer lending the unsecured nature of loans as well as the relative novelty of the platforms make the assessment of risk a difï¬ cult problem. In this article we propose to use traditional machine learning methods enhanced with fuzzy set theory based transformation of data to improve the quality of identifying loans with high likelihood of default. We assess the proposed approach on a real-life dataset from one of the largest peer-to-peer platforms in Europe. The results demonstrate that (i) traditional classiï¬ cation algorithms show good performance in classifying borrowers, and (ii) their performance can be improved using linguistic data transformation
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2018.169
dc.identifier.isbn 978-0-9981331-1-9
dc.identifier.uri http://hdl.handle.net/10125/50056
dc.language.iso eng
dc.relation.ispartof Proceedings of the 51st Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Machine Learning and Network Analytics in Finance
dc.subject Machine learning, fuzzy logic, peer-to-peer lending, classification
dc.title Credit Risk Evaluation in Peer-to-peer Lending With Linguistic Data Transformation and Supervised Learning
dc.type Conference Paper
dc.type.dcmi Text
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