Most retirement planning tools that are available today use a fixed rate of return to calculate total savings amount at retirement. This calculation is fine as a starting point, but needs to be refined to provide investors with a better estimate of their retirement outlook. Because the fixed rate of return calculation relies on a single average estimate of the rate of return, there is a possibility to underestimate the amount of savings and investments required at retirement.

The Monte-Carlo model can evaluate potential shortfall situations by applying standard statistical techniques and calculate the probability of achieving the retirement goal. Graphically show projection of client savings into the future. With the simulation calculate hundreds of random scenarios based on the historical performance of the asset class mix in each retirement account.

The efficient frontier model can help you choose a portfolio that gives you the greatest possible rate of return for the given risk tolerance.

It is a given that volatility of your investment portfolio will increases your risk of loss of principal, so all other things being equal, you would like to minimize volatility in your portfolio. Of course the problem is that there is another effect that works in the opposite direction: if you limit yourself to low-risk securities, you'll be limiting yourself to investments that tend to have low rates of return. So what you really want to do is include some higher growth, higher risk securities in your portfolio, but combines them in a smart way, so that some of their fluctuations cancel each other out. (In statistical terms, you're looking for a combined standard deviation that's low, relative to the standard deviations of the individual securities.) The result should give you a high average rate of return, with less of the harmful fluctuations.