Credit risk prediction in an imbalanced social lending environment

DOI

10.2991/ijcis.11.1.70How to use a DOI?

Keywords

Risk prediction; peer-to-peer lending; imbalance classification; resampling

Abstract

Credit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan, particularly in peer-to-peer lending where class imbalance problems are prevalent. However, few credit risk prediction models for social lending consider imbalanced data and, further, the best resampling technique to use with imbalanced data is still controversial. In an attempt to address these problems, this paper presents an empirical comparison of various combinations of classifiers and resampling techniques within a novel risk assessment methodology that incorporates imbalanced data. The credit predictions from each combination are evaluated with a G-mean measure to avoid bias towards the majority class, which has not been considered in similar studies. The results reveal that combining random forest and random under-sampling may be an effective strategy for calculating the credit risk associated with loan applicants in social lending markets.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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TY  - JOUR
AU  - Anahita Namvar
AU  - Mohammad Siami
AU  - Fethi Rabhi
AU  - Mohsen Naderpour
PY  - 2018
DA  - 2018/03/26
TI  - Credit risk prediction in an imbalanced social lending environment
JO  - International Journal of Computational Intelligence Systems
SP  - 925
EP  - 935
VL  - 11
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.11.1.70
DO  - 10.2991/ijcis.11.1.70
ID  - Namvar2018
ER  -