연도 | 2022 |
학회지 | Risks |
저자 | Jaewon Park, Minsoo Shin |
| 권(Vol) | 10
|
| 호(No) | 13
|
쪽(P.P)
| 1-20
|
The risk-based capital (RBC) ratio, an insurance company’s financial soundness system,
evaluates the capital adequacy needed to withstand unexpected losses. Therefore, continuous
institutional improvement has been made to monitor the financial solvency of companies and protect
consumers’ rights, and improvement of solvency systems has been researched. The primary purpose
of this study is to find a set of important predictors to estimate the RBC ratio of life insurance
companies in a large number of variables (1891), which includes crucial finance and management
indices collected from all Korean insurers quarterly under regulation for transparent management
information. This study employs a combination of Machine learning techniques: Random Forest
algorithms and the Bayesian Regulatory Neural Network (BRNN). The combination of Random
Forest algorithms and BRNN predicts the next period’s RBC ratio better than the conventional
statistical method, which uses ordinary least-squares regression (OLS). As a result of the findings
from Machine learning techniques, a set of important predictors is found within three categories:
liabilities and expenses, other financial predictors, and predictors from business performance. The
dataset of 23 companies with 1891 variables was used in this study from March 2008 to December
The risk-based capital (RBC) ratio, an insurance company’s financial soundness system,
evaluates the capital adequacy needed to withstand unexpected losses. Therefore, continuous
institutional improvement has been made to monitor the financial solvency of companies and protect
consumers’ rights, and improvement of solvency systems has been researched. The primary purpose
of this study is to find a set of important predictors to estimate the RBC ratio of life insurance
companies in a large number of variables (1891), which includes crucial finance and management
indices collected from all Korean insurers quarterly under regulation for transparent management
information. This study employs a combination of Machine learning techniques: Random Forest
algorithms and the Bayesian Regulatory Neural Network (BRNN). The combination of Random
Forest algorithms and BRNN predicts the next period’s RBC ratio better than the conventional
statistical method, which uses ordinary least-squares regression (OLS). As a result of the findings
from Machine learning techniques, a set of important predictors is found within three categories:
liabilities and expenses, other financial predictors, and predictors from business performance. The
dataset of 23 companies with 1891 variables was used in this study from March 2008 to December