신용정보에 따른 입원 및 수술 발생 상대위험도 적용방안 연구: 신용정보원 데이터 이용
A Study on How to Apply the Relative Risk of Hospitalization and SurgeryAccording to Credit Information: Using Korea Credit Information Services Data
0
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
본 연구는 신용정보원의 신용정보와 보험정보를 이용하여 기존에 개발된 보험상품의 건강상태에 따른 질병발생 상대위험도에 보험가입고객의 신용정보를 추가하는 방법을 제안하고자 한다. 보험계약의 유지자를 대상으로 독립적으로 개인별 신용상태(연체, 대출, 신용 등)에 영향을 받는 입원 및 수술 발생 등 2개의 통계적 예측모형을 개발하여 개별 집단의 평균 보험사고 발생률과 전체 집단의 보험사고 발생률의 차이로 정의되는 상대위험도를 계산하였다. 데이터 활용의 제한으로 인하여 기존의 건강상태에 따른 입원/수술 발생과 새롭게 산출하는 신용상태에 따른 입원/수술 발생이 독립이라는 조건하에, 신용정보에 따른 보험사고 발생 상대위험도를 건강상태에 따른 보험사고 발생 상대위험도에 곱함으로써 신용정보를 보험사고 상대위험도에 포함하고자 하였다. 보험사들은 독립적으로 신용상태에 따른 보험사고 발생 상대위험도와 건강상태에 따른 보험사고 발생 상대위험도를 보험상품 가격의 할인 등의 긍정적인 면으로 이용할 수 있을 것이다.
Abstract
This study proposes a method to add the credit information of insurance customers to the relative risk of disease occurrence according to health status of previously developed insurance products using credit information and insurance information of credit information sources. We developed two statistical prediction models for the occurrence of hospitalization and surgery that are independently affected by individual credit status (delinquency, loan, credit, etc.) targeting the holder of the insurance contract, and calculated the average insurance accident incidence rate of the individual group and the insurance accident rate of the entire group relative risk, defined as the difference in incidence. Due to limitations in data utilization, the relative risk of insurance accidents according to credit information is calculated under the condition that hospitalization/surgery according to existing health conditions and hospitalization/surgery according to newly calculated credit status are independent. By multiplying the relative risk of occurrence, credit information intended to be included in the relative risk of insurance accidents. Insurance companies will be able to independently use the relative risk of an insured event according to credit status and the relative risk of an insured event according to health status as a positive aspect, such as discounting insurance product prices.
참고문헌 (14)
- 이상연, "국민건강보험 빅데이터를 활용한 주요 만성질환 발생률 예측모형의 개발과 활용*", 보험학회지, (133), pp.23~48 (2023), 한국보험학회[DOI]
- 정재욱, "생명보험 언더라이팅 시 개인신용정보의 활용 효과 분석", 금융연구, 25(1), pp.25~56 (2011), 한국금융학회
- 정재욱, "개인신용정보와 개인용자동차보험 사고발생확률 및 손해율 간의 상관관계 연구", 리스크관리연구, 20(2), pp.17~58 (2009), 한국리스크관리학회[DOI]
- Dean, L. T., "Consumer credit, chronic disease and risk behaviours", Journal of Epidemiology Community Health, 73(1), pp.73~78 (2019)
- Dean, L. T., "Using Credit Scores to Understand Predictors and Consequences of Disease", American Journal of Public Health; Washington, 108(11), pp.1503~1505 (2018)
- Dean, L. T., "Consumer credit as a novel marker for economic burden and health after cancer in a diverse population of breast cancer survivors in the USA", Journal of Cancer Survivorship, 12(3), pp.306~315 (2018)
- Dobkin, C, "The economic consequences of hospital admissions", America Economic Review, 108(2), pp.308~352 (2018)
- Golden, L. L., "Empirical Evidence on the Use of Credit Scoring for Predicting Insurance Losses with Psychosocial and Biochemical Explanations", North American Actuarial Journal, 20(3), pp.233~251 (2016)
- Houle, J. N., "Flu and finances : influenza outbreaks and loan defaults in US cities, 2004-2012", American Journal of Public Health, 105(9), pp.75~80 (2015)
- Israel, S., "Credit scores, cardiovascular disease risk, and human capital", Psychological and Cognitive Sciences, 111(48), pp.17087~17092 (2014)
- Li, Y., "Sound credit scores and financial decisions despite cognitive aging", Economic Science, 112(1), pp.65~69 (2015)
- Knapp, E. A., "Consumer credit scores as a novel tool for identifying health in urban U. S. neighborhoods", Annals of Epidemiology, 28(10), pp.724~729 (2018)
- Roberts, T., "predictive modelling and text mining to detect fraud in the insurance industry", Ph.D, pp.2011
- Wu, C. P., "Does Credit Score Really Explain Insurance Losses? Multivariate Analysis from a Data Mining Point of View", Casualty Actuarial Society Forum Winter, pp.113~138 (2003)