Optimal Counterfactual Explanations for Scorecard modelling

TL;DR

This paper develops mathematical programming methods to generate optimal, diverse, and realistic counterfactual explanations for scorecard models used in banking, enhancing interpretability and decision transparency.

Contribution

It introduces mixed-integer programming formulations for scorecard models to produce multiple, diverse counterfactual explanations with desired properties, a novel approach in this context.

Findings

Effective generation of diverse counterfactuals confirmed on real datasets

Approach achieves realistic and sparse explanations within practical CPU times

Method enhances interpretability for scorecard models in banking

Abstract

Counterfactual explanations is one of the post-hoc methods used to provide explainability to machine learning models that have been attracting attention in recent years. Most examples in the literature, address the problem of generating post-hoc explanations for black-box machine learning models after the rejection of a loan application. In contrast, in this work, we investigate mathematical programming formulations for scorecard models, a type of interpretable model predominant within the banking industry for lending. The proposed mixed-integer programming formulations combine objective functions to ensure close, realistic and sparse counterfactuals using multi-objective optimization techniques for a binary, probability or continuous outcome. Moreover, we extend these formulations to generate multiple optimal counterfactuals simultaneously while guaranteeing diversity. Experiments on two real-world datasets confirm that the presented approach can generate optimal diverse counterfactuals addressing desired properties with assumable CPU times for practice use.