온라인 상거래 데이터를 반영한 개인신용평가모형(커머스스코어) 개발
Development of a Personal Credit Scoring Model(COMMERCE Score) Using On-Line Commerce Data
1
조회
0
다운로드
10
인용
0
참고문헌
키워드
초록
개인신용평가모형은 금융소비자의 미래 부도발생가능성을 평점 형태로 예측하는 수리적인 모형이다. 은행 등 금융회사는 개인신용평가모형을 활용해서 대출 승인 여부, 한도 및 금리 등을 결정한다. 일반적으로 개인신용평가모형을 구성하는 항목은 금융기관이 집중하는 신용정보 등이며, 최근 금융취약계층을 바르게 평가하기 위해 추가적인 평가항목으로 상거래, 통신 데이터 등 비금융정보가 반영되기 시작했다. 본 연구에서는 비금융정보 중 상거래 데이터를 반영한 새로운 개념의 개인신용평가모형즉, 커머스스코어를 실제 데이터에 기반하여 개발하고, 금융정보만으로 개발된 모형보다 상대적으로 높은 성능을 보임을 실증분석하였다. 또한 이를 활용해서 금융소비자의 리스크를 보다 정교하게 측정하고 효율적인 대출심사전략 수립이 가능함을 확인했다.
Abstract
Personal Credit Scoring Model is a mathematical model which predicts the Financial Consumer’s probability of default in a form of score. Banks and other financial institutions use personal credit scoring model to make decisions for loan approval, setting credit limit and interest rate, etc. Generally, a credit scoring model is consist of credit information which financial institutions provide to credit bureau companies(CB). Recently, alternative data such as commerce or telecom data are included as evaluation elements in the model. In this research, we developed a COMMERCE score using real on-line commerce data and proved that it has a relatively high performance. By using this model, we suggested the risk of financial consumer can be more elaborately measured and a more effective personal loan approval strategy can be established.
참고문헌 (21)
- Re-definition of integrated Credit Information Concentration, "Korea Institute of Finance (KIF)", 2~ (2015)
- A Study on the Improvement of the Credit Information Concentration Systems, "Korea Institute of Finance(KIF)", 18~ (2014)
- Lyn C. Thomas, "A Survey of credit and behavioural scoring : forecasting financial risk of lending to customers", International Journal of Forecasting, 16, pp.149~172 (2000)
- A Study on the Comprehensive Improvement of Personal Credit Rating System, "Financial Service Commission", ~ (2018)
- NICE Information Service Co., Ltd, "Majority Monitoring Targets: An Analysis and Implications", NICE VIP Forum (2018)
- A Study on the Revitalization of Credit Information Interference for the Revitalization of Data Economy, "Financial Service Commission", ~ (2018)
- Seung-Yeop Baek, "A Study on the Protection and Use of Personal Credit Information in Big Data Society", Ph.D., pp.2017
- Michal A. Turner, "New to Credit from Alternative Data", PERC, ~ (2019)
- Changes in the Personal Credit Rating Model and Its Implications, "Sung-Min Choi", Credit Information Services, ~, pp.Issue Report, Vol.4 (2018)
- Will the Global Social Credit Rating Systems Grow?, "Korea Association of Wireless Internet Business", ~, pp.The Mobile Issue Report, Vol.11 (2012)
- Understanding and Future of the Telecom Score, "Young-Ok Jung", NICE CB Research Institute, ~ (2017)
- Entrepreneurial Finance Lab(EFL), "Alternative Credit Scoring in Emerging Market", ~ (2015)
- Durand D., "Risk Elements in Consumer Installment Financing" (1941), National Bureau of Economic Research
- Lewis, E. M., "An Introduction to Credit Scoring" (1992), Athena Press
- Accelerating Financial Inclusion with New Data, "European Microfinance Network (EFL)", Center for Financial Inclusion at Accion Institute of International Finance, ~ (2018)
- Xinhai Liu, "A Credit Scoring Model Based on Alternative Mobile Data for Financial Inclusion", ~ (2017)
- Behavior Revealed in Mobile Phone Usage Predicts Credit Repayment, "Daniel Bjorkegren", ~ (2015)
- 권영준, "개인신용평가에서의 비금융정보의 경제적 효과", 한국경제연구, 29(2), pp.81~107 (2011), 한국경제연구학회
- Office for Government Policy Coordination, Prime Minister’s Secretariat, "Guidelines for Non-Identification of Personal Information", ~ (2017)
- The Progress and practical considerations of Basel 2 – Implementation by Korean Banks of the PD Model, "Korea Institute of Finance(KIF)", ~ (2007)