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송서하(고려대학교) 김준홍(고려대학교) 김형석(고려대학교) 박재선 강필성(고려대학교)

DOI : 10.7232/JKIIE.2019.45.3.248

UCI(KEPA) : I410-ECN-0101-2019-530-000760705

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초록

The early warning model, one of the risk management models for financial companies, was introduced to detect in advance the capital adequacy crisis of financial institutions. The existing early warning models have mainly used predictive models with structured data-based variables such as various macroeconomic indicators and enterprise internal indicators. The purpose of this study is to further improve the performance of the early warning models by applying customer complaints data and news articles related to individual financial institutions to machine learning-based model. As a result of applying this technique to actual data over the period of 2001 to 2017, the methodology proposed in this study has demonstrated a performance improvement up to 20%p compared to the existing methodology in certain evaluation metrics.

목차

1. 서론
2. 선행 연구
3. 방법론
4. 실험 설계
5. 실험 결과
6. 결론 및 활용방안
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