금융업의 합성 데이터 유용성 분석: 온라인 P2P 대출연체 분석을 중심으로

• 영문명: Utility of Synthetic Data in Finances: An Application of Online P2P Lending Loan Default Analysis
• 발행기관: 한국IT서비스학회
• 저자명: 송민채(Minchae Song)
• 간행물 정보: 『한국IT서비스학회지』제23권 제4호, 55~70쪽, 전체 16쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2024.08.31

국문 초록

In order to promote the AI applications in the financial industry, the financial sector has recently been paying attention to synthetic data technology. Synthetic data generates using a purpose-built mathematical model or algorithm, with the aim of solving a set of data science tasks. This study evaluates the utility of synthetic data by analyzing heterogeneous tabular data that is composed of discrete, categorical and continuous variables and has the feature of unbalanced data, which is commonly found in the financial sector. As a synthetic data generation technique, the TGAN and CTGAN models are applied by considering the feature of tabular data. As a result of evaluating the utility in terms of resemblance and machine learning efficiency, those of TGAN are confirmed to be high, while the quality of CTGAN are relatively poor. This is interpreted to be particularly due to the generation of categorical variables, and it suggests that how those with categorical properties especially are considered in the synthetic data generation model is a major factor in determining the utility of generation synthetic data.

목차

1. 서론

2. 선행연구

3. 연구설계

4. 분석결과

5. 결론

참고문헌

키워드

Synthetic Data Personal Information GAN CTGAN TGAN