An Interpretable Loan Credit Evaluation Method Based on Rule Representation Learner
This work addresses interpretability challenges for financial institutions and borrowers in loan credit evaluation, though it is incremental as it builds on existing rule-based and neural network techniques.
The authors tackled the problem of unreliable post-hoc interpretability methods in high-stake fields by designing an intrinsically interpretable model based on Rule Representation Learner (RRL) for loan credit evaluation on the Lending Club dataset. The results show their model outperforms interpretable decision trees in performance and is close to black-box models, while also demonstrating that post-hoc methods are not always reliable.
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.