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With the increasing scarcity of conventional energy and environmental degradation, countries around the world are increasing their investment in renewable energy development. In order to make a scientific investment evaluation of renewable energy projects, this paper examines the analysis and control of their financial data. The intelligent analysis system of financial data is constructed based on OLAP. Logistic regression model and decision tree algorithm model are selected as the operation algorithm of the system to complete the intelligent analysis of data. Combining random forest algorithm and autoregressive moving average model, under the guidance of Bagging idea, the financial status of renewable energy projects after investment is judged in order to achieve the purpose of dynamic control. According to the results of analysis and control of financial data of renewable energy projects, it is known that the correct probability of intelligent analysis of financial data reached 94.5%, 83.1%, and 92.7% for different sample sizes of data sets, respectively. There were significant improvements in the efficiency of capital usage and asset quality, with an increase in capital concentration of 30.42%, an increase in inventory turnover from 10.68% to 13.04%, and an increase in the recovery rate of overdue accounts receivable from 60.31% to 67.83%. It has been proven that the method can help investors to better utilize uncertainty to improve the investment value of project, providing investors with a new way of thinking about decision-making.

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.

The purpose of this study is to find the most important variables that represent the future projections of the Bank of International Settlements’ (BIS) capital adequacy ratio, which is the index of financial soundness in a bank as a comprehensive and important measure of capital adequacy. This study analyzed the past 12 years of data from all domestic banks in South Korea. The research data include all financial information, such as key operating indicators, major business activities, and general information of the financial supervisory service of South Korea from 2008 to 2019. In this study, machine learning techniques, Random Forest Boruta algorithms, Random Forest Recursive Feature Elimination, and Bayesian Regularization Neural Networks (BRNN) were utilized. Among 1929 variables, this study found 38 most important variables for representing the BIS capital adequacy ratio. An additional comparison was executed to confirm the statistical validity of future prediction performance between BRNN and ordinary least squares (OLS) models. BRNN predicted the BIS capital adequacy ratio more robustly and accurately than the OLS models. We believe our findings would appeal to the readership of your journal such as the policymakers, managers and practitioners in the bank-related fields because this study highlights the key findings from the data-driven approaches using machine learning techniques.