One of the biggest problems in the continuity of one’s education is the education fee which is often unaffordable. Therefore, the existence of education insurance is a solution to this problem. Along with increasing public interest in education insurance, insurance companies need to adjust the claims reserves with the number of claims paid to maintain the company’s capital. Claim reserves are funds that must be provided by insurance companies to fulfil obligations to policy holders in the future. Losses and inaccuracies in the payment of insurance claims will result in the policy holder and the insurance company itself. Therefore, it is necessary to do a prediction of insurance company’s monthly reserve claims. In education insurance, the claim reserve data has seasonal characteristics and the number of educational insurance claims tends to increase at the turn of the school year. These fluctuating patterns are supposed to fit the application of the SARIMA model and the nonparametric regression model with the Fourier series estimator in forecasting. Fourier series is a function that has flexibility in approaching fluctuating, seasonal, and recurring data patterns. The results showed that the prediction accuracy of the SARIMAX model was higher than the nonparametric regression model with MAPE of 15% and 4% respectively.

Risk assessment and developing predictive models for diabetes prevention is considered an important task. Therefore, we proposed to analyze and provide a comprehensive analysis of the performance of diabetes screening scores for risk assessment and prediction in five populations: the Chinese, Japanese, Korean, US-PIMA Indian, and Trinidadian populations, utilizing statistical and machine learning (ML) methods. Additionally, due to the present COVID-19 epidemic, it is necessary to investigate how diabetes and COVID-19 are related to one another. Thus, by using a sample of the Korean population, the interrelationship between diabetes and COVID-19 was further investigated. The results revealed that by using a statistical method, the optimal cut points among Chinese, Japanese, Korean, US-PIMA Indian, and Trinidadian populations were 6.205 mmol/L (FPG), 5.523 mmol/L (FPG), and 5.375% (HbA1c), 150.50–106.50 mg/dL (FBS), 123.50 mg/dL (2hPG), and 107.50 mg/dL (FBG), respectively, with AUC scores of 0.97, 0.80, 0.78, 0.85, 0.79, and 0.905. The results also confirmed that diabetes has a significant relationship with COVID-19 in the Korean population (p-value 0.001), with an adjusted OR of 1.21. Finally, the overall best ML models were performed by Naïve Bayes with AUC scores of 0.736, 0.75, and 0.83 in the Japanese, Korean, and Trinidadian populations, respectively.

The cash flow analysis is essential to examine the economic flows in the financial system. In this paper, the financial dataset at Bank Rakyat Indonesia was used, it recorded the sources of cash inflow and outflow during a particular period. The univariate time series model like the autoregressive and integrated moving average is the common approach to build the prediction based on the historical dataset. However, it is not suitable to estimate the multivariate dataset and to predict the extreme cases consisting of nonlinear pairs between independent-dependent variables. In this study, the comparison of using two types of models i.e., transfer function and artificial neural network (ANN) were investigated. The transfer function model includes the coefficient of moving average (MA) and autoregressive (AR), which allows the multivariate analysis. Furthermore, the artificial neural network allows the learning paradigm to achieve optimal prediction. The financial dataset was divided into training (70%) and testing (30%) for two types of models. According to the result, the artificial neural network model provided better prediction with achieved root mean square error (RMSE) of 0.264897 and 0.2951116 for training and testing respectively.

Type 2 diabetes (T2D) and non-alcoholic fatty liver disease (NAFLD) are worldwide chronic diseases that have strong relationships with one another and commonly exist together. Type 2 diabetes is considered one of the risk factors for NAFLD, so its occurrence in people with NAFLD is highly likely. As the high and increasing number of T2D and NAFLD, which potentially followed by existing together number, an analysis and assessment of T2D screening scores in people with NAFLD is necessary to be done. To prevent this potential case, an effective early prediction model is also required to be developed, which could help the patients avoid the dangers of both existing diseases. Therefore, in this study, analysis and assessment of T2D screening scores in people with NAFLD and the early prediction model utilizing a forward logistic regression-based feature selection method and multi-layer perceptrons are proposed. Our analysis and assessment results showed that the prevalence of T2D among patients with NAFLD was 8.13% (for prediabetes) and 37.19% (for diabetes) in two population-based NAFLD datasets. The variables related to clinical tests, such as alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), and systolic blood pressure (SBP), were found to be statistically significant predictors (p-values < 0.001) that indicate a strong association with T2D among patients with NAFLD in both the prediabetes and diabetes NAFLD datasets. Finally, our proposed model showed the best performance in terms of all performance evaluation metrics compared to existing various machine learning models and also the models using variables recommended by WHO/CDC/ADA, with achieved accuracy as much as 92.11% and 83.05% and its improvement scores after feature selection of 1.35% and 5.35%, for the first and second dataset, respectively.

Investor demand for gold as a safe investment tool tends to increase during the Covid-19 pandemic. In addition, global uncertainty due to the trade war between the US and China also affects the movement of gold prices which makes gold prices tend to fluctuate. Modeling and prediction of gold prices are very important to minimize risk factors. The 10 years range of data (2001-2021) was analyzed. It was found that the conditions of the trade war between the US and China, as well as the Covid-19 pandemic had a significant impact on the movement of gold prices. By using these two variables as exogenous variables in the Autoregressive Integrated Moving Average with Exogenous Variable (ARIMAX) approach, we get a model that does not meet the classical assumptions of parametric time series analysis, which is the serial correlation in the squared residual indicating heteroskedasticity. To overcome this problem, the Autoregressive Conditional Heteroscedasticity – Generalized Autoregressive Conditional Heteroscedasticity (ARCH-GARCH) approach is used to capture the effect of volatility. The final model is ARIMAX(4,2,1)-GARCH(0,2) with MAPE value of 3.53% and 5.76% for training and testing data. Thus, the model has been able to predict gold prices with high accuracy.

Background: The life insurance sector often experiences nonlinear fluctuations during macroeconomic shocks and disasters. To capture these dynamics, nonlinear methods such as the Markov switching model are appropriate. Objective: This study examines the effects of premium adequacy and investment returns on the claim payment ratio in Indonesia’s life insurance sector using a Markov switching approach. Methods: This study analyzed 115 monthly observations of premium adequacy, investment returns, and claim payment ratios from January 2016 to July 2025, obtained from the Otoritas Jasa Keuangan (OJK). Three Markov switching models (MSM, MSV, and MSMV) were compared to identify the dominant source of regime-switching behavior. The two-regime Markov switching variance model was selected based on convergence and the lowest Akaike Information Criterion (AIC). Results: The optimal model identified a volatile regime and a stable regime. The volatile regime had a 75.87% persistence probability with an average duration of four months, whereas the stable regime showed a 90.52% persistence probability and an average duration of 10.5 months. The probability of transitioning from volatility to stability (24.13%) exceeded the probability of shifting from stability to volatility (9.48%). Conclusion: Indonesia’s conventional life insurance sector demonstrates resilience by maintaining stable conditions and recovering from periods of volatility.