  Peer-to-peer (P2P) lending platforms have grown rapidly over the past decade
as the network infrastructure has improved and the demand for personal lending
has grown. Such platforms allow users to create peer-to-peer lending
relationships without the help of traditional financial institutions. Assessing
the borrowers' credit is crucial to reduce the default rate and benign
development of P2P platforms. Building a personal credit scoring machine
learning model can effectively predict whether users will repay loans on the
P2P platform. And the handling of data outliers and sample imbalance problems
can affect the final effect of machine learning models. There have been some
studies on balanced sampling methods, but the effect of outlier detection
methods and their combination with balanced sampling methods on the
effectiveness of machine learning models has not been fully studied. In this
paper, the influence of using different outlier detection methods and balanced
sampling methods on commonly used machine learning models is investigated.
Experiments on 44,487 Lending Club samples show that proper outlier detection
can improve the effectiveness of the machine learning model, and the balanced
sampling method only has a good effect on a few machine learning models, such
as MLP.
