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.

1. 서 론
2. 이론적 배경
    2.1 기업부도예측 선행연구
    2.2 랜덤포레스트(Random Forest)
    2.3 XGBoost(eXtreme Gradient Boosting)
    2.4 서포트벡터머신(Support Vector Machine)
    2.5 인공신경망(Artificial Neural Network)
    2.6 SMOTE(Synthetic Minority Over samplingTechnique)
    2.7 LIME
3. 기업부도예측 방법론
    3.1 데이터 전처리
    3.2 특성 선택
    3.3 모델 및 하이퍼파라미터 후보군 선정
    3.4 데이터 불균형을 고려한 분류모델 결정
    3.5 개선 우선순위 도출
4. 사례적용
    4.1 데이터 전처리
    4.2 특성 선택
    4.3 분류 모델 후보군 및 하이퍼파라미터 선정
    4.4 데이터 불균형을 고려한 분류모델 결정
    4.5 개선 우선순위 도출
5. 결 론
Acknowledgement
References

• Sun-Woo Heo(한양대학교 일반대학원 경영컨설팅학과) | 허선우
• Dong Hyun Baek(한양대학교 경상대학 경영학부) | 백동현 Corresponding Author