A Novel Reject Inference Model Using Outlier Detection and Gradient Boosting Technique in Peer-to-Peer Lending

@article{Xia2019ANR,
  title={A Novel Reject Inference Model Using Outlier Detection and Gradient Boosting Technique in Peer-to-Peer Lending},
  author={Yufei Xia},
  journal={IEEE Access},
  year={2019},
  volume={7},
  pages={92893-92907}
}
Credit scoring is an efficient tool in handling the information asymmetry of peer-to-peer (P2P) lending. Credit scoring models are typically built only with the accepted applicants, which may cause sample bias and further hinder the predictive performances. Reject inference methods utilize the information contained in the rejected samples by inferring their potential status and incorporate them with the accepted samples. In this study, we propose a novel reject inference model (i.e., OD… CONTINUE READING

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