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Data analytic approach for bankruptcy prediction

Title
Data analytic approach for bankruptcy prediction
Authors
Son, H.Hyun, C.Phan, D.Hwang, H.J.
Date Issued
Dec-2019
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Abstract
Bankruptcy prediction problem has been intensively studied over the past decades. From traditional statistical models to state of the art machine learning models, various predictive models are developed and applied to various datasets. However, models that use machine learning are not used in the field of business, for two main reasons. First, the prediction accuracy does not far exceed the statistical models and second, the results are not interpretable. In this study, we focused on solving the skewness which is a characteristic of financial data. By solving this problem, we obtained 17% average improvement in AUC over existing models. To address the second shortcoming, we analyze the importance of features identified by the XGBoost model. The interpretation of the model differs among categories of data. Our bankruptcy prediction model has high predictive accuracy with clear explanations and is therefore directly applicable to the industry. (C) 2019 Elsevier Ltd. All rights reserved.
URI
http://oasis.postech.ac.kr/handle/2014.oak/100212
ISSN
0957-4174
Article Type
Article
Citation
EXPERT SYSTEMS WITH APPLICATIONS, vol. 138, 2019-12
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 HWANG, HYUNG JU
Dept of Mathematics
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