This study aims to improve the interpretability and transparency of forecasting results by applying an explainable AI technique to corporate default prediction models. In particular, the research addresses the challenges of data imbalance and the economic cost asymmetry of forecast errors. To tackle these issues, predictive performance was analyzed using the SMOTE-ENN imbalance sampling technique and a cost-sensitive learning approach. The main findings of the study are as follows. First, the four machine learning models used in this study (Logistic Regression, Random Forest, XGBoost, and CatBoost) produced significantly different evaluation results depending on the degree of asymmetry in forecast error costs between imbalance classes and the performance metrics applied. Second, XGBoost and CatBoost showed good predictive performance when considering variations in prediction cost asymmetry and diverse evaluation metrics. In particular, XGBoost showed the smallest gap between the actual default rate and the default judgment rate, highlighting its robustness in handling class imbalance and prediction cost asymmetry. Third, SHAP analysis revealed that total assets, net income to total assets, operating income to total assets, financial liability to total assets, and the retained earnings ratio were the most influential factors in predicting defaults. The significance of this study lies in its comprehensive evaluation of predictive performance of various ML models under class imbalance and cost asymmetry in forecast errors. Additionally, it demonstrates how explainable AI techniques can enhance the transparency and reliability of corporate default prediction models.

1. 서 론
2. 이론적 배경
    2.1 통계적 방법 중심의 기업부도예측 연구
    2.2 기계학습 기반의 기업부도예측 연구
    2.3 설명가능 인공지능을 적용한 연구
3. 연구 방법
    3.1 예측모델
    3.2 클래스 불균형 접근법
    3.3 Performance Metrics
    3.4 설명가능 인공지능
4. 데이터
    4.1 부도 기업의 정의
    4.2 표본 구성
    4.3 부도예측 변수 선정
    4.4 전처리 및 불균형 샘플링
    4.5 기초통계
5. 분석 결과
    5.1 불균형 샘플링 전후 데이터 비교
    5.2 모델 성과 비교
    5.3 SHAP을 이용한 결과 해석
6. 결 론
Acknowledgement
References

• Jong Chul Yune(Department of Management Consulting, Graduate School of Hanyang University) | 윤종철 (한양대학교 일반대학원 경영컨설팅학과)
• Dong Hyun Back(Division of Business Administration, Hanyang University ERICA) | 백동현 (한양대학교 ERICA 경상대학 경영학부) Corresponding author