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

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Publication Details

List of Authors: Mezei Jozsef, Byanjankar Ajay, Heikkilä Markku
Place: University of Hawaii at Manoa
Publication year: 2018
Publisher: Proceedings of the Annual Hawaii International Conference on System Sciences
Book title: Proceedings of the Annual Hawaii International Conference on System Sciences
Title of series: Hawaii International Conference on System Sciences 2018
Start page: 1366
End page: 1375
ISBN: 978-0-9981331-1-9


The widespread availability of various peer-to-peer lending solutions is
rapidly changing the landscape of financial 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 financial 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 difficult 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 classification algorithms show good performance in
classifying borrowers, and (ii) their performance can be improved using
linguistic data transformation


Classification, Finance, Fuzzy logic, Machine learning, Network Analytics, Peer-to-peer lending

Last updated on 2020-28-03 at 04:37