ABSTRACT
Personal credit risk assessment is an important part
of the development of financial enterprises. Big data credit investigation is
an inevitable trend of personal credit risk assessment, but some data are
missing and the amount of data is small, so it is difficult to train. At the
same time, for different financial platforms, we need to use different models
to train according to the characteristics of the current samples, which is
time-consuming. In view of these two
problems, this paper uses the idea of transfer learning to build a transferable
personal credit risk model based on Instance-based Transfer Learning (Instance-based
TL). The model balances the weight of the samples in the source domain, and
migrates the existing large dataset samples to the target domain of small
samples, and finds out the commonness between them. At the same time, we have
done a lot of experiments on the selection of base learners, including
traditional machine learning algorithms and ensemble learning algorithms, such
as decision tree, logistic regression, xgboost and so on. The datasets are from P2P
platform and bank, the results show that the AUC value of Instance-based TL is
24% higher than that of the traditional machine learning model, which fully
proves that the model in this paper has good application value. The model’s
evaluation uses AUC, prediction, recall, F1. These criteria prove that this
model has good application value from many aspects. At present, we are trying
to apply this model to more fields to improve the robustness and applicability
of the model; on the other hand, we are trying to do more in-depth research on
domain adaptation to enrich the model.