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Presenting Advice Using Monte Carlo Simulation in AdviserGo

This guide explains how to use the Monte Carlo Insight in AdviserGo to present advice to clients and demonstrate how changes to retirement age and portfolio choice impact the probability of success and long-term asset values.

Scenario Overview

In this example:

• The clients have a current net worth of £1.6 million, including both liquid and illiquid assets
• Their base plan assumes retirement at age 65
• The clients are considering:
  • Retiring at age 63 instead of 65
  • Investing their ISAs and pension assets in Portfolio 2 or Portfolio 4

To explore these options, two What-If plans have been created:

• Retire at age 63 using Portfolio 2
• Retire at age 63 using Portfolio 4

Comparing Cash Flow Outcomes

Using Let’s See → Compare Plans, the two What-If scenarios are compared.

Key observations:

• No cash flow shortfalls are projected in either scenario
• If shortfalls existed, they would be indicated by red markers in the charts

From a cash flow perspective, the conclusion is that:

• Retiring at age 63 works for the clients
• Both Portfolio 2 and Portfolio 4 support the plan without projected shortfalls

Comparing Net Worth Outcomes

Next, the net worth projections for the two portfolios are compared.

• Portfolio 2 produces a more conservative long-term outcome
• Portfolio 4 produces higher projected asset values, reflecting higher risk and return assumptions

The charts clearly show how different asset allocations lead to different long-term outcomes, even when cash flow affordability is the same.

Preparing to Run the Monte Carlo Insight

Before running the Monte Carlo Insight, ensure the following:

• Liquid invested assets have an asset allocation defined
• The plan includes goals for Monte Carlo to test against

Monte Carlo is used to test whether the plan can meet its goals across a wide range of randomized market outcomes.

Running Monte Carlo: Portfolio 2 (Retire at 63)

To run the insight:

1. Open the Monte Carlo Insight
2. Select the plan using Portfolio 2
3. Choose the number of iterations
   • For speed, this example uses 50 iterations
   • You can select anywhere from 50 to 1,000 iterations

Monte Carlo measures how often the plan successfully meets all goals across the simulated return paths.

Results

• Probability of success: 98%

Notes:

• Results may vary slightly each time Monte Carlo is run
• You can hover over any year to view:
  • Minimum expected outcome
  • Maximum expected outcome

Running Monte Carlo: Portfolio 4 (Retire at 63)

Next, the same process is repeated for the plan using Portfolio 4.

Results

• Probability of success: 94%

Again, hovering over individual years shows the range of potential outcomes.

Viewing Yearly Probability of Success

You can change the Monte Carlo chart view to show Yearly Probability of Success.

This view highlights:

• How probability remains close to 100% in earlier retirement years
• Where probability begins to decline later in life (for example, in the clients’ 80s)

This can be especially helpful when discussing longevity risk and long-term sustainability with clients.

Comparing Monte Carlo Results

By comparing the two scenarios:

• Portfolio 2 shows a higher probability of success (98%)
• Portfolio 4 shows a lower probability of success (94%)

This comparison helps clients understand the trade-off between:

• Higher potential returns
• Greater variability and risk

Using Monte Carlo to Support Advice Conversations

The Monte Carlo Insight helps advisers:

• Present advice visually and objectively
• Show how strategic decisions affect outcomes
• Compare scenarios using probability rather than single-path projections
• Support informed client decision-making

Need Help?

If you have questions or need assistance:

• Email support@planwithvoyant.com, or
• Open a plan, click the client name in the top-right corner, and select Request Support
• Enter your question and share client access if appropriate