4 CONCLUSIONS
In this paper, several machine learning methods for
P2P default prediction are discussed, and challenges
in this field as well as solutions are proposed.
In method section, four machine learning methods
are introduced, namely decision tree, support vector
machine, deep neural networks and ensemble
learning, and it also includes how the researchers start
from data cleaning, go through data balancing, feature
selection and other steps, and finally build a model.
Then, the three challenges faced in the field of
P2P lending default prediction are discussed, namely
interpretability, that is, most machine learning
algorithms lack interpretability and make the model
untrustworthy; applicability, that is, a model cannot
be directly applied to a specific P2P lending platform;
and privacy, that is, user data privacy issues. And then
corresponding solutions are proposed. When facing
the interpretability problem, four approaches for
explaining machine learning algorithms can be used;
when facing the applicability problem, domain
adaptation (a machine learning method that only the
domain is different, but the task remains unchanged)
can be applied; when facing the privacy problem,
federated learning can be used to improve data
privacy security.
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