Case Exercise: Flexible Spending Accounts

A Flexible Spending Account (FSA) is an account where employees can put money away for later use on certain health care costs. This money is not subject to federal income tax and can help reduce taxes paid by an employee. The federal government allows an employee to put up to $2,650 into the account at the beginning of each year, but only can be used for health care expenses. Any money left in the account at the end of the year is considered an “overage” and is forfeited (lost). If the health care expenses are more than the amount in the account (i.e. an “underage”), the employee must pay for the excess out of his/her own pocket. This excess payment can be claimed in an income tax return to receive tax credit at the rate of 25% of the excess payment.

Your are a financial planner working with a client to determine how much money they should allocate to their FSA for the upcoming year. Your client did not keep much data on their previous health care expenses, but they do know their annual spending for the last 5 years was at least $1,000 and at most $3,000.

Assume that for this client their income is taxed at a 40% rate.

Learning Objectives:

To forecast what will be the outcome under different scenarios, or different levels of confidence levels by using various tools such as the Normal distribution properties, confidence intervals, or probabilities.

Statistics Needed:

Rank order confidence intervals to address lack of data (only min and max spending for last 5 years).

Data Sources:

Tasks:

Answer the questions below to determine the likelihood of “overage” or “underage” for a client’s FSA account under different scenarios. Based on the results make a recommendation on how the clients can use their FSA account for max benefits.

Use the 6 steps of the business analytics process as a framework for answering the questions below and justifying your recommendation to your client.

Step 1. Recognizing the problem

Any outside factors to consider that may affect this next years health expenses?
Describing the relationship between employee contribution and tax rate

Step 2. Defining the problem

If we had to have an overage or underage, which would lose less money?
What are assumptions we can make about health care spending?

Step 3. Structuring the problem

Can we assume a normal distribution for expenses?
Without our clients data what assumptions can we make?
What are other constraints?

Step 4. Analyzing the problem

What are the assumptions we need to make?
How will they affect our models?

Step 5. Interpreting Results and Making a Decision

How confident can we be in our results?
What assumptions were made and how do they affect these results?

Step 6. Implementing the solution

How can we measure success or failure of our recommendation?
Is there anything to consider that may change our recommendation?

Questions to answer:

Explain if it is reasonable to assume a normal distribution for health care expenses. Think about the general population and consider rank-order confidence intervals to explain how to make assumptions about your clients expenses based on the mean or median of those around a similar age.
If your client puts $2,650 (the maximum amount) into their FSA, how likely are they to use it all within the year?
Is it possible for your client to put enough money into their FSA to cover all health care expenses for any given year at least 95% of the time? How much money should be put into their FSA to achieve their goal?
How much money should be put into the FSA if your client wanted to spend all the money in the account at least 50% of the time?
Considering the costs of an “underage” (every $1 extra that you have to pay out of pocket is extra money to be taxed but deductible) or an “overage” (where all money left over is forfeited but untaxable). Is it better to have more money than necessary in the account, or less? Explain.
How much should your client put into their FSA to maximize benefits for their partial unknown health expenses? With this amount how often will it cover all of their health care expenses?
If the FSA rules change and allow for $500 of unspent money in an FSA to rollover into the following year, does that change the optimal amount of money for your client to put into the account?

Report

Write a professional report for your client. The report should be to the point and give specific, actionable advice or solutions based on the data and analytics. Avoid technical aspects and terms that are non-essential and any speculations not substantiated by the data. This report should be concise without lengthy explanations being necessary to understand it.

There is no min or max page limit as charts and tables can take up a highly variable amount of space. However, any charts or tables included need to be understandable to a layman at first glance (labeled and captioned if needed). The particular models you use, interpretations, and advice given are your choice and you should be prepared to explain or defend this if needed!

Use this as an outline for the report:

A. Description of the business problem

What is the range of expenses we’re looking at and are they reliable?
What are the consequences of over or under estimating the amount to put into the FSA?
What questions do we wanted answered and how do these explicitly help address the decision?

B. Data, methods, and models and results

Discuss the basic approach used to analyze the data and any concerns about the integrity and quality of the data used. For example, “There is concern about predicting clients expenditures in the past 5 years because there is no specific data provided. With that limitation, we are just making predictions without real historical information.”
Describe the models used (put the formula, tables, graphs, etc. here) indicating what they are used for. e.g. “This calculation determines the probability of clients to used up all fund at least x% of the time.”
Provide detailed answers to the decision questions using the models as reference
Indicate all “important” assumptions made and why you think they are reasonable. An assumption is important if: you cannot get a result without the assumption e.g. “we must assume our client can be reflected as an average consumer of health care based on their estimates of previous expenses.” OR if the assumption is wrong or invalid your results would be affected significantly. For example, the assumption “with or without FSA deposits our client will still be taxed at the same income tax rate” if wrong or invalid, would significantly affect the amount of money we should deposit into the FSA.
Do not list technical assumptions used for statistical analysis e.g. “We assume the data is normally distributed.”

C. Decision making

Explain specifically how you used the models and results to make your decisions. This may be literal results such as “Never fully fund the FSA because the possibility to use it all is only 20%”.
Detail special considerations or issues to watch out for e.g. “There is not enough data to accurately predict clients overage or underage possibility.”
Describe how the improvements or benefits from your solution can be measured or observed. e.g “Since it is more financially sound to have an ‘underage’ the client can put less than the recommended amount of money into the FSA and measure how much is left at the end of the year to see how accurate the optimal amount would have been”.