Earticle

초록

영어

This study proposes an interpretable framework for corporate bankruptcy prediction by integrating machine learning and explainable artificial intelligence (XAI) techniques. While prior bankruptcy prediction studies have primarily focused on improving predictive accuracy using static financial indicators, limited attention has been given to incorporating dynamic financial trends and translating model interpretation results into practical decision- making frameworks. To address this limitation, this study applies a flatten strategy that transforms recent three-year financial data into a structured vector representation while preserving temporal information. Based on this approach, an XGBoost model was developed using financial statement variables, financial ratios, growth indicators, and delta variables reflecting year-over-year changes. To address the severe class imbalance problem inherent in bankruptcy prediction, repeated undersampling experiments were conducted, and model performance was evaluated using Recall and G-Mean, which are particularly suited for imbalanced classification tasks. The empirical results show that the dataset combining static and dynamic financial variables achieved the best predictive performance, particularly in Recall and G-Mean. SHAP analysis further revealed that market value, liquidity, profitability, and capital structure variables play critical roles in bankruptcy prediction. In particular, delta variables related to profitability and growth trends demonstrated high explanatory power, suggesting that corporate bankruptcy is more closely associated with the deterioration trajectory of financial conditions rather than a snapshot of financial status at a single point in time. Based on the SHAP analysis results, this study systematizes bankruptcy-related financial variables into five interpretive categories: firm size, liquidity, profitability, capital structure, and financial trend. This categorization provides a practical interpretation framework that goes beyond binary bankruptcy prediction, explaining through which financial dimensions risk emerges and supporting financial risk diagnosis and decision-making. This study contributes to the literature by integrating temporal financial trends with interpretable machine learning and by proposing a practical SHAP-based financial risk interpretation framework for corporate bankruptcy analysis.

목차

Abstract
1. 서론
2. 이론적 배경
2.1 전통적 기업부도 예측 연구
2.2 동적 재무정보 기반 부도예측 연구
2.3 시계열 기반 기업부도 예측 연구
2.4 모델 해석 및 실무 의사결정 활용 연구
2.5 본 연구의 접근 및 차별성
3. 연구방법
3.1 데이터 설명
3.2 분석데이터 구성
3.3 XGBoost 모델링
4. 모델 검증 방법 및 분석결과
4.1 평가지표
4.2 기업부도 예측 결과
4.3 SHAP 분석 및 결과 해석
5. 결론
5.1 연구의 의의
5.2 연구의 한계 및 향후 연구 방향
References
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키워드

저자

• 최지인 [ Jiin Choi | M.S., Business Analyst, Containers Marketing Agency ] First Author
• 이한희 [ Hanhee Lee | Adjunct Professor, School of Knowledge Management, Chung-Ang Univ, 84, Heukseok-ro, Dongjak-gu, Seoul, Director of LKP Management Institute ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  JITAM [Journal of Information Technology Applications and Management]

• 간기

  격월간

• pISSN

  1598-6284

• eISSN

  2508-1209

• 수록기간

  1999~2026

• 십진분류

  KDC 005 DDC 005