Credit risk prediction in an imbalanced social lending environment
- 10.2991/ijcis.11.1.70How to use a DOI?
- Risk prediction; peer-to-peer lending; imbalance classification; resampling
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.
- © 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 -