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

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7 Citeringar (Scopus)

Sammanfattning

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

OriginalspråkOdefinierat/okänt
Titel på gästpublikationProceedings of the Annual Hawaii International Conference on System Sciences
FörlagProceedings of the Annual Hawaii International Conference on System Sciences
Sidor1366–1375
ISBN (tryckt)978-0-9981331-1-9
DOI
StatusPublicerad - 2018
MoE-publikationstypA4 Artikel i en konferenspublikation
EvenemangHawaii International Conference on System Sciences - 51st Hawaii International Conference on System Sciences
Varaktighet: 3 jan 20186 jan 2018

Konferens

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

Nyckelord

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

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