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경영대학
경영학전공
Journal papers
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Feature-Weighted Counterfactual-Based Explanation for Bankruptcy Prediction
Title
Feature-Weighted Counterfactual-Based Explanation for Bankruptcy Prediction
Authors
Cho S.H.
;
Shin K.-S.
Ewha Authors
신경식
SCOPUS Author ID
신경식
Issue Date
2023
Journal Title
Expert Systems with Applications
ISSN
0957-4174
Citation
Expert Systems with Applications vol. 216
Keywords
Bankruptcy prediction
;
Counterfactual-based explanation
;
Explainable artificial intelligence
Publisher
Elsevier Ltd
Indexed
SCIE; SCOPUS
Document Type
Article
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. © 2022
DOI
10.1016/j.eswa.2022.119390
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