@article{M8BA54934,
title = "A Study on Classification Models for Predicting Bankruptcy Based on XAI",
journal = "KIPS Transactions on Software and Data Engineering",
year = "2023",
issn = "2287-5905",
doi = "https://doi.org/10.3745/KTSDE.2023.12.8.333",
author = "Jihong Kim/Nammee Moon",
keywords = "Big Data, Classification Model, eXplainable AI, Bagging, Boosting, Ensemble",
abstract = "Efficient prediction of corporate bankruptcy is an important part of making appropriate lending decisions for financial institutions 
and reducing loan default rates. In many studies, classification models using artificial intelligence technology have been used. In the 
financial industry, even if the performance of the new predictive models is excellent, it should be accompanied by an intuitive explanation 
of the basis on which the result was determined. Recently, the US, EU, and South Korea have commonly presented the right to request 
explanations of algorithms, so transparency in the use of AI in the financial sector must be secured. In this paper, an artificial 
intelligence-based interpretable classification prediction model was proposed using corporate bankruptcy data that was open to the outside 
world. First, data preprocessing, 5-fold cross-validation, etc. were performed, and classification performance was compared through 
optimization of 10 supervised learning classification models such as logistic regression, SVM, XGBoost, and LightGBM. As a result, LightGBM 
was confirmed as the best performance model, and SHAP, an explainable artificial intelligence technique, was applied to provide a 
post-explanation of the bankruptcy prediction process."
}