Credit risk prediction in an imbalanced social lending environment
This addresses credit risk assessment for social lending platforms, but it is incremental as it builds on existing methods with a specific evaluation metric.
The paper tackled credit risk prediction in imbalanced peer-to-peer lending data by empirically comparing classifier-resampling combinations, finding that random forest with random under-sampling was effective based on G-mean evaluation.
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.