A Collaborative Path Forward: Integrating Monte Carlo Modeling, the Actuarial Approach, and Copilot

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Monte Carlo modeling has been the backbone of retirement planning for decades. It is powerful, familiar, and deeply embedded in advisor workflows. Yet as David Blanchett recently argued in “Successfully Failing,” the traditional “probability of success” metric can be misleading. A plan may show a high success rate while delivering a lifestyle far below what the client actually wants. As Blanchett suggests, traditional Monte Carlo modeling can “succeed,” while failing the client.

This is the central issue: Monte Carlo tells us whether a plan is likely to remain solvent, but not whether it supports the life the client wants to live.

Where Monte Carlo Falls Short — and Why Advisors Need More Than Probabilities

Blanchett highlights several structural weaknesses in the traditional Monte Carlo framework. These can be grouped into four broad categories that advisors routinely encounter:

Outcome interpretation issues — Success is defined as avoiding depletion, not achieving the desired lifestyle. The binary framing of “success” versus “failure” hides the full spectrum of outcomes.
Modeling limitations — Real households adjust spending, yet Monte Carlo often assumes they don’t. Guaranteed income sources may be excluded or inconsistently modeled. Spending is frequently treated as static, ignoring real-world variability.
Incomplete household representation — Important assets, liabilities, and distinctions between needs and wants may be omitted.
Weak alignment with client preferences — Clients care about funding lifestyle needs with certainty and often prefer front-loaded spending while they are younger and more active.

These limitations do not invalidate Monte Carlo. They simply illustrate that Monte Carlo alone may not deliver a solid retirement plan, even when enhanced by Blanchett’s proposed refinements.

How the Actuarial Approach Complements Monte Carlo

The actuarial approach reframes planning around funded status, liability matching, and year-by-year sustainability, particularly for retired and near-retired households. Instead of asking whether the plan “succeeds,” the actuarial approach asks:

Are the client’s desired spending levels funded by their resources?
Are essential expenses covered by non-risky assets or guaranteed income?
How does funded status evolve over time?
What adjustments — up or down — are prudent given the client’s risk capacity and preferences?

This structure directly addresses the shortcomings Blanchett identifies:

Funded status replaces binary success metrics.
Year-by-year spending paths replace vague solvency probabilities.
Adjustments reflect real-world spending flexibility.
The framework centers on client preferences, not abstract probabilities.

A Moderate but Important Clarification: The Accumulation Stage

While the actuarial approach can be applied during the accumulation stage, its advantages are generally less important for households that are still building assets. Accumulators face uncertain future earnings, evolving savings patterns, and retirement liabilities that are not yet well-defined. As a result, funded-status metrics and liability matching are most valuable for retirees and near-retirees, where spending needs are clearer, guaranteed income sources are known, and annual sustainability assessments provide actionable guidance.

Why Advisors Should Combine Monte Carlo and the Actuarial Approach

The most effective planning model is not a replacement — it is a collaboration:

Monte Carlo (traditional or Blanchett-enhanced) provides uncertainty modeling and stress testing.
The actuarial approach provides structure, interpretability, and annual guidance.

Together, they create a richer discovery process that helps advisors uncover what clients truly want and need.

This integrated approach allows advisors to:

Anchor planning in a clear funded-status metric
Use Monte Carlo to explore variability around that anchor
Translate results into actionable spending and investment guidance
Communicate adjustments in a way clients can understand and trust

Monte Carlo remains indispensable for modeling uncertainty; the actuarial approach provides the structure Monte Carlo lacks, particularly for retired and near-retired clients.

Where Copilot Strengthens the Advisor’s Work

Copilot enhances this collaborative model by giving advisors a real-time analytical partner:

Recalculates funded status instantly
Summarizes Monte Carlo outputs in plain language
Documents client preferences and translates them into spending paths
Runs side-by-side comparisons of alternative strategies
Helps prepare clearer explanations and client-friendly visuals

For example, Copilot can instantly revalue funded status after a change in spending, Social Security timing, or asset allocation — tasks that normally require multiple manual recalculations.

The advisor remains in control. Copilot simply accelerates the analysis and frees advisor time for deeper client conversations — the part of planning that Monte Carlo alone cannot solve.

A Better Experience for Clients

When advisors integrate Monte Carlo, the actuarial approach, and Copilot, clients benefit from:

More stable and comprehensible spending guidance
A clearer understanding of how choices affect long-term sustainability
A planning process that reflects their actual wants and needs
A framework that adapts gracefully as life circumstances change

This “deeper dive” is the future of retirement planning — not a new model replacing an old one, but a more holistic approach that blends rigorous analytics with important client input. Advisors who adopt this integrated model will find that it deepens client conversations, clarifies tradeoffs, improves long-term decision-making and adds significant value to the client relationship.

Read more by Ken Steiner:

Use This 3-Step Process to Develop Better End-of-Life Assumptions for Your Clients

Ken Steiner is a retired actuary with a website titled, "How Much Can I Afford to Spend in Retirement?"

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