Feature-Weighted Counterfactual-Based Explanation for Bankruptcy Prediction

Soo Hyun Cho, Kyung shik Shin

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

In recent years, there have been many studies on the application and implementation of machine learning techniques in the financial domain. Implementation of such state-of-the-art models inevitably requires interpretability for users to understand the result and trust. However, as most of the machine learning methods are “black-box,” explainable AI, which aims to provide explanations to users, has become an important research issue. This paper focuses on explanation by counterfactual example for a bankruptcy-prediction model. Counterfactual-based explanation offers an alternative case for users in order for them to have a desired output from the model. This paper proposes a genetic algorithm (GA)-based counterfactual generation algorithm using feature importance whilst taking other key factors into account. Feature importance was derived from a prediction model, and key factors for counterfactuals include closeness to the original dataset and sparsity. The proposed method presented advantages over the nearest contrastive sample and a simple counterfactual generation algorithm in the experiment. Also, it provides relevant and compact explanations to enhance the interpretability of the bankruptcy prediction model.

Original languageEnglish
Article number119390
JournalExpert Systems with Applications
Volume216
DOIs
StatePublished - 15 Apr 2023

Bibliographical note

Publisher Copyright:
© 2022

Keywords

  • Bankruptcy prediction
  • Counterfactual-based explanation
  • Explainable artificial intelligence

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