Model-Agnostic Counterfactual Explanations in Credit Scoring

Abstract

The past decade has shown a surge in the use and application of machine learning and deep learning models across various domains. One such domain is credit scoring, where applicants are scored to assess their creditworthiness for loan applications. It is essential to ensure that no biases or discriminations are incurred during the scoring process. Most machine learning and deep learning models are prone to unintended bias and discrimination in the datasets. Therefore, it is imperative to explain each prediction from the models during the scoring process to avoid the element of model bias and discrimination. Our study proposes a novel optimization formulation that generates sparse counterfactual explanations via a custom genetic algorithm to explain the black-box model’s predictions. We evaluated the efficacy of the proposed method on publicly available credit scoring datasets by comparing the counterfactual explanations generated by the proposed method with explanations from credit scoring experts. The proposed counterfactual explanation method does not only explain rejected loan applications but also can be used to explain approved loan applications.