데이터 불균형을 고려한 설명 가능한 인공지능 기반 기업부도예측 방법론 연구

• 영문명: A Methodology for Bankruptcy Prediction in Imbalanced Datasets using eXplainable AI
• 발행기관: 한국산업경영시스템학회
• 간행물 정보: 『산업경영시스템학회지』제45권 제2호, 65~76쪽, 전체 12쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2022.06.30

영문 초록

Recently, not only traditional statistical techniques but also machine learning algorithms have been used to make more accurate bankruptcy predictions. But the insolvency rate of companies dealing with financial institutions is very low, resulting in a data imbalance problem. In particular, since data imbalance negatively affects the performance of artificial intelligence models, it is necessary to first perform the data imbalance process. In additional, as artificial intelligence algorithms are advanced for precise decision-making, regulatory pressure related to securing transparency of Artificial Intelligence models is gradually increasing, such as mandating the installation of explanation functions for Artificial Intelligence models. Therefore, this study aims to present guidelines for eXplainable Artificial Intelligence-based corporate bankruptcy prediction methodology applying SMOTE techniques and LIME algorithms to solve a data imbalance problem and model transparency problem in predicting corporate bankruptcy. The implications of this study are as follows. First, it was confirmed that SMOTE can effectively solve the data imbalance issue, a problem that can be easily overlooked in predicting corporate bankruptcy. Second, through the LIME algorithm, the basis for predicting bankruptcy of the machine learning model was visualized, and derive improvement priorities of financial variables that increase the possibility of bankruptcy of companies. Third, the scope of application of the algorithm in future research was expanded by confirming the possibility of using SMOTE and LIME through case application.

목차

1. 서 론

2. 이론적 배경

3. 기업부도예측 방법론

4. 사례적용

5. 결 론

Acknowledgement

References

키워드

Bankruptcy Prediction Data Imbalance eXplainable AI SMOTE LIME Machine Learning