Interpretable Credit Application Predictions With Counterfactual Explanations

TL;DR

This paper enhances interpretability of credit application predictions by introducing positive counterfactuals and weighting strategies, making explanations more understandable for both approved and rejected loans.

Contribution

It proposes positive counterfactuals for explaining accepted applications and introduces weighting strategies to improve counterfactual interpretability.

Findings

Outperforms baseline in generating smaller, more interpretable counterfactuals

Effective for both approved and rejected loan explanations

Demonstrated on HELOC dataset

Abstract

We predict credit applications with off-the-shelf, interchangeable black-box classifiers and we explain single predictions with counterfactual explanations. Counterfactual explanations expose the minimal changes required on the input data to obtain a different result e.g., approved vs rejected application. Despite their effectiveness, counterfactuals are mainly designed for changing an undesired outcome of a prediction i.e. loan rejected. Counterfactuals, however, can be difficult to interpret, especially when a high number of features are involved in the explanation. Our contribution is two-fold: i) we propose positive counterfactuals, i.e. we adapt counterfactual explanations to also explain accepted loan applications, and ii) we propose two weighting strategies to generate more interpretable counterfactuals. Experiments on the HELOC loan applications dataset show that our contribution outperforms the baseline counterfactual generation strategy, by leading to smaller and hence more interpretable counterfactuals.