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

Research output: Chapter in Book/Conference proceedingConference contributionScientificpeer-review

Abstract

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

Original languageUndefined/Unknown
Title of host publicationProceedings of the Annual Hawaii International Conference on System Sciences
PublisherProceedings of the Annual Hawaii International Conference on System Sciences
Pages1366–1375
ISBN (Print)978-0-9981331-1-9
DOIs
Publication statusPublished - 2018
MoE publication typeA4 Article in a conference publication
EventHawaii International Conference on System Sciences - 51st Hawaii International Conference on System Sciences
Duration: 3 Jan 20186 Jan 2018

Conference

ConferenceHawaii International Conference on System Sciences
Period03/01/1806/01/18

Keywords

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

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