@inproceedings{2c7d90d307e84c399d394375b9bf1f9e,
title = "Detection of Defaulters in P2P Lending Platforms using Unsupervised Learning",
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.",
author = "Partha Mukherjee and Youakim Badr",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2022 ; Conference date: 01-08-2022 Through 03-08-2022",
year = "2022",
doi = "10.1109/COINS54846.2022.9854964",
language = "English (US)",
series = "2022 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2022",
address = "United States",
}