  The interpretability of model has become one of the obstacles to its wide
application in the high-stake fields. The usual way to obtain interpretability
is to build a black-box first and then explain it using the post-hoc methods.
However, the explanations provided by the post-hoc method are not always
reliable. Instead, we design an intrinsically interpretable model based on
RRL(Rule Representation Learner) for the Lending Club dataset. Specifically,
features can be divided into three categories according to their
characteristics of themselves and build three sub-networks respectively, each
of which is similar to a neural network with a single hidden layer but can be
equivalently converted into a set of rules. During the training, we learned
tricks from previous research to effectively train binary weights. Finally, our
model is compared with the tree-based model. The results show that our model is
much better than the interpretable decision tree in performance and close to
other black-box, which is of practical significance to both financial
institutions and borrowers. More importantly, our model is used to test the
correctness of the explanations generated by the post-hoc method, the results
show that the post-hoc method is not always reliable.
