Detection of Defaulters in P2P Lending Platforms using Unsupervised Learning

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

The lenders and the borrowers favor the P2P lending platforms unlike the traditional lending as P2P lending framework incurs low cost and quick initiation of loans. However the P2P lending platform suffers from a problem that refers to the default borrowers who can't replay the loans and hence generates the financial loss to the investors. In our research we employed four unsupervised learning techniques 1) self-organizing map 2) density based spatial clustering, 3) elliptic envelope and 4) auto-encoders on the Lending club dataset by reducing the features using recursive feature elimination in order to detect the anomalies in form of default borrowers. Our results show that self organizing map is the best performer in detecting the potential defaulters with precision 0.79 and recall 0.816.

Original languageEnglish (US)
Title of host publication2022 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665483568
DOIs
StatePublished - 2022
Event2022 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2022 - Barcelona, Spain
Duration: Aug 1 2022Aug 3 2022

Publication series

Name2022 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2022

Conference

Conference2022 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2022
Country/TerritorySpain
CityBarcelona
Period8/1/228/3/22

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Information Systems
  • Information Systems and Management
  • Control and Optimization

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