  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.
