Towards Global Explanations for Credit Risk Scoring
This addresses the need for transparent credit risk scoring for financial institutions, though it appears incremental as it builds on existing explanation techniques.
The paper tackles the problem of explaining black-box credit risk classifiers by developing a method that samples decision functions to learn interpretable alternative models while maintaining classification accuracy. They demonstrate feasibility using a private mortgage default dataset.
In this paper we propose a method to obtain global explanations for trained black-box classifiers by sampling their decision function to learn alternative interpretable models. The envisaged approach provides a unified solution to approximate non-linear decision boundaries with simpler classifiers while retaining the original classification accuracy. We use a private residential mortgage default dataset as a use case to illustrate the feasibility of this approach to ensure the decomposability of attributes during pre-processing.