The purpose of this report was to determine what is the optimal amount of money that should be deposited in the flexible savings account (FSA). The benefit of the FSA is that the money deposited in the account are tax free and can be used to pay for medical expenses. However, if the deposited amount is not used up by the end of a year, the money is lost. Therefore, it is crucial to test what amount is best to be deposited using different risk models.
Two types of Monte Carlo simulations were conducted to determine the optimal deposit amount. A Monte Carlo simulation is used to predict the probability of a variety of outcomes (Arend & Schäfer, 2019). Monte Carlo Simulation is an effective tool that can be used in business decision-making (al-Bourini et al.,2020). In business, it is primarily used for forecasting purposes (Fabianova et al., 2019). First, normal distribution simulation using @Risk in Microsoft Excel was conducted. The inputs were the annual salary of $80,000, a 30% tax rate, and a mean annual medical expense of $2,000 with a standard deviation of $500. The tested values included $1000, $1200, $1400, $1600, $1800, and $2000. The results of the analysis are provided in Table 1 below.
Table 1. Standard deviation risk analysis results
Thus, the results demonstrate that the best option would be to deposit $1,000, as it maximizes the retained salary.
Second, gamma distribution analysis was conducted with the same input. The results of the analysis are provided in Table 2 below.
Table 2. Gamma distribution risk analysis results
The analysis revealed that $1,000 remained the best option for the FSA deposit, as it maximizes the retained salary.
Thus, it is recommended to contribute $1,000 to the FSA if the annual medical expenses are at the level of $2,000 with a standard deviation of $500.
References
al-Bourini, F. A., Aljawarneh, N. M., Bourini, I., Almaaitah, M. F., & kaderAlomari, K. A. (2020). Directing Strategic Decision and Perceived Faculty Performance Using PLS Analysis and Monte Carlo Simulation in Jordanian Private Universities. Journal of Talent Development and Excellence, 12, 2235-2252.
Arend, M. G., & Schäfer, T. (2019). Statistical power in two-level models: A tutorial based on Monte Carlo simulation. Psychological methods, 24(1), 1.
Fabianova, J., Kacmary, P., & Janekova, J. (2019). Operative production planning utilising quantitative forecasting and Monte Carlo simulations. Open Engineering, 9(1), 613-622.