Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/258324 
Year of Publication: 
2022
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 10 [Issue:] 1 [Article No.:] 13 [Publisher:] MDPI [Place:] Basel [Year:] 2022 [Pages:] 1-20
Publisher: 
MDPI, Basel
Abstract: 
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 2018 with quarterly updates for each year.
Subjects: 
life insurance companies
Bayesian Regulatory Neural Network
Random Forest algorithms
RBC ratio
corporate sustainable management
Machine learning
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
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