The population of many countries might undergo dramatic changes in the coming decades due to continuous increases in life expectancy. The sustained reduction in mortality rates and its systematic underestimation has been attracting the significant interest of researchers in recent years because of its potential impact on population size and structure, social security systems, and (from an actuarial perspective) the life insurance and pensions industry worldwide. Among all projection methods, the Lee-Carter method has been widely accepted by the actuarial community. This paper explores the use of the Lee-Carter method to forecast the mortality rates for Malaysian population. The index of the level of mortality for each gender, and the shape and sensitivity coefficients for 18 age groups were obtained through the Lee-Carter method. The Singular Values Decomposition (SVD) is used to forecast the general index for the time period that goes from 2011 to 2030. Since the model involves nonlinear equations that are explicitly difficult to solve, the Matrix Laboratory Version 7.0 (MATLAB 7.0) software will be used in the study. The empirical data sets of Malaysia population for the period of 1981-2010 and for both genders will be considered.

Myocardial infarction (MI) and stroke are two of the top causes of death globally, including countries in Asia. These diseases are part of the cardiovascular disease and share similar risk factors such as hypertension, hypercholesterolemia, diabetes, BMI, and smoking. Each country in Asia region has a different rate of death caused by stroke and myocardial disease, which is related to the prevalence of risk factors among their population. By investigating the trends and distribution of CVD death rate and the prevalence of risk factors in selected countries, we are able to identify that hypertension and smoking are indeed important risk factors for stroke and MI. Although glucose level, cholesterol level, and BMI are not able to reflect the distribution of CVD death rate well, they must have an association with stroke and MI to some extent. Practicing healthy lifestyle and avoiding risk factors should be able to reduce the risk of death by stroke or MI.

The vital role of Malaysian Employees’ Provident Fund (EPF) is to provide financial support for its participants during retirement years. However, the issues of inadequacy have risen and EPF has been through various improvements in order to cope with current living situations, including making adjustment in the contribution rates. This study intends to provide the projection of EPF accumulations for three different types of contribution rates namely contribution rates at current fixed rate, increasing and decreasing proportion. Then, the replacement ratio is calculated and is used as an indicator to determine the adequacy of retirement income delivered by EPF. The ideal replacement ratio recommended by financial advisors is at 70%. Based on the findings in this study, contribution rates following a decreasing proportion gives replacement ratio that exceeds or nearest to the ideal replacement ratio, while contribution rates at current fixed rate gives replacement rates that fall far from 70%. Therefore, this study shows that the accumulated amount in the fund with contribution rates following a decreasing proportion gives higher replacement ratio and is recommended to be applied by the Malaysian EPF.

Myocardial infarction (MI) is among the top causes of death in Malaysia. The mortality rate following MI was high, especially within the first 30 days after the onset. This paper study the ability of k-Nearest Neighbors (kNN) and Naïve Bayes algorithms to predict the 30-day mortality of MI patients, using. The dataset used for this study is provided by National Cardiovascular Disease Database (NCVD) which consist of 2840 MI patients from hospitals in Malaysia. The sudden death predictions made by the machine learning are based on the age, gender, year of onset, smoking habit, BMI, diabetes, hypertension and cholesterol level. The result suggests that kNN algorithm has better performance in predicting the sudden death compared to Naïve Bayes. The number of independent variables plays an important role in mortality prediction, and removing insignificant variables improve the performance.Â

In the realm of financial management, optimizing the allocation of funds in banking companies is vital to their operational efficiency. Banks manage their funds by allocating them into reserve and credit funds as the main activities of banking. Optimizing these allocations ensures that all assets are effectively utilized. However, real-life optimization problems often involve uncertainty, making deterministic data assumptions insufficient. Robust Optimization is a methodology that addresses these uncertainties by incorporating computational tools to solve optimization problems with uncertain data. The uncertainty approach used in robust optimization is polyhedral sets. In the context of banking, uncertainties influencing the allocation of reserve and credit funds include financial risks and returns. These risks can be quantified using Conditional Value-at-Risk (CVaR), a suitable measure for banking fund allocation due to its ability to accommodate varying risk characteristics under different business conditions. This study focuses on developing an optimization model for reserve and credit fund allocation in banking companies using a Multi-objective Robust CVaR approach with lexicographic, informed by business risk data and credit instruments. The resulting optimization model yields optimal allocations for reserve and credit funds, ensuring efficient asset utilization to support banking operations. This approach offers new perspectives for banks to achieve fund allocations that are not only regulatory compliant but also optimal. The implications of such optimal allocations include mitigating risks associated with reserve fund imbalances and enhancing profitability through optimal credit returns.

The increase in the number of older populations has raised concerns globally. This demographic changes is due to the increase in life expectancy and declines in fertility as well as mortality rates. Malaysia is also currently experiencing the demographic changes and these changes leads to concerns on the uncertaintiness of future family care and the need for long-term care services. Therefore, this study investigates on the level of awareness on financing long term care cost between rural and urban residential area and marital status amomg respondents in Malaysia. 452 responds were analysed using multiple linear regiression model to identify the variables that influenced the level of acceptance and awareness towards long-term care cost. Findings show that dependency towards retirement income is significant from urban-rural residents and among single respondents. Health status is also significant for both rural-urban and marital status. However, literacy towards the long term care cost is significant among respondents residing in urban areas only and also married couples. The findings are beneficial and relevant to demonstrate on the importance of financial planning to fund for future long-term care costs.