# Towards Global Explanations for Credit Risk Scoring

arXiv: https://arxiv.org/abs/1811.07698 · HTML: https://mlsanity.com/paper/1811.07698/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2018.

## Built on by (most cited first, 2 of 2)

- [Desiderata for Explainable AI in statistical production systems of the European Central Bank](https://mlsanity.com/paper/2107.08045/) (2021)
- [Introducing explainable supervised machine learning into interactive feedback loops for statistical production system](https://mlsanity.com/paper/2202.03212/) (2022)

## Abstract (arXiv)

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.