How do I set up an advice engine for retirement income planning and advisor recommendations across our planning team?

Here’s a practical way to set up an advice engine for retirement income planning and advisor recommendations across a planning team. I’ll frame it as a build plan you can implement incrementally.

1) Define the two decisions the engine must support

Split the system into two linked but distinct outputs:

1. Retirement income advice

   • How much can the client spend?
   • Which accounts should withdrawals come from?
   • When should Social Security be claimed?
   • What withdrawal strategy fits the client?
   • What risk actions are needed if markets drop or spending changes?

2. Advisor recommendation / routing

   • Which advisor or team should handle the case?
   • Who has the right specialization, capacity, licensing, and client fit?
   • Should the client be routed to a generalist, retirement specialist, tax-focused planner, or high-net-worth advisor?

Keeping these separate helps you govern them independently.

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2) Establish the planning rules and the recommendation policy

You need a written decision policy before you automate anything.

For retirement income planning

Define the core rules:

• Withdrawal hierarchy rules
• Required minimum distribution handling
• Social Security claiming assumptions
• Tax-aware withdrawal sequencing
• Glidepath / equity exposure assumptions
• Guardrails for income spending adjustments
• Longevity assumptions
• Inflation assumptions
• Healthcare / long-term care assumptions
• Spousal survivor planning rules

For advisor recommendations

Define the routing rules:

• Client complexity score thresholds
• Asset size bands
• Geographic licensing constraints
• Retirement vs accumulation specialization
• Business-owner, equity compensation, or tax complexity flags
• Capacity/load balancing rules
• Relationship continuity rules
• Conflict-of-interest rules
• Minimum service model by segment

Put these in a policy document that business, compliance, and advisors all sign off on.

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3) Build a common client data model

The engine will only be as good as the data feeding it.

Retirement planning data

Capture:

• Age, marital status, dependents
• Retirement date target
• Income sources: pensions, Social Security, annuities, rental income
• Account types: taxable, traditional IRA/401(k), Roth, HSA
• Balances, cost basis, embedded gains
• Spending needs: essential vs discretionary
• Tax bracket and filing status
• Health status and longevity assumptions
• Employer benefits, survivor benefits
• Risk tolerance and sequence-of-returns sensitivity
• Estate goals and charitable intent

Advisor routing data

Capture:

• Client segment
• Planning complexity score
• Net worth / investable assets
• Needed specialization
• Geographic location
• Product or service needs
• Current advisor relationship
• Advisor workload and availability
• Compliance restrictions

Normalize this into a single household record plus planning metadata.

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4) Create a rules engine for the deterministic layer

Start with a rules engine for clear, auditable decisions.

Examples:

• If age > 73 and traditional IRA exists, flag RMD planning.
• If spouse is more than 5 years younger, adjust Social Security/survivor assumptions.
• If guaranteed income covers essential expenses, recommend discretionary spending guardrails.
• If client has concentrated stock > X%, trigger tax diversification review.
• If household complexity score > threshold, route to senior retirement specialist.

This layer should:

• Be explainable
• Be version-controlled
• Support override with documented reasons
• Log every recommendation and the reason code

Tools could be a rules service, decision tables, or a policy engine.

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5) Add a quantitative retirement income model

For actual retirement income advice, you usually need a simulation or optimization layer.

Core calculations

• Cash flow projection by month/year
• Tax projection
• Account depletion forecast
• Monte Carlo probability of success
• Safe spending range
• Withdrawal sequence comparison
• Social Security claiming scenario comparison
• Required reserve buffer analysis

Useful outputs

• “Base case sustainable income”
• “Conservative income”
• “Stretch income”
• “Probability of sustaining income through age 95”
• “Withdrawal source recommendation”
• “Action if portfolio drops 15%”

You can build this as:

• Deterministic cash flow model for baseline
• Monte Carlo engine for risk
• Optimization layer for tax and withdrawal sequencing

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6) Create a recommendation score for advisor matching

For advisor assignment, use a scoring model instead of hard rules alone.

Example dimensions

• Retirement specialization fit
• Tax expertise fit
• Complexity handling fit
• Client size fit
• Relationship continuity score
• Geographic/license fit
• Capacity fit
• Responsiveness/SLA fit
• Language or cultural fit if relevant

Example formula

You can score each advisor from 0–100 for each household:

• 30% specialization fit
• 20% complexity fit
• 15% capacity availability
• 15% client segment fit
• 10% relationship continuity
• 10% geographic/compliance fit

Then choose the top candidate, subject to rule-based constraints.

Use hard constraints first, scoring second.

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7) Separate “recommendation generation” from “human approval”

For both retirement advice and advisor assignment, keep humans in the loop.

Workflow

1. Engine generates recommendation
2. Advisor/planner reviews
3. Planner can accept, modify, or override
4. Override reason is captured
5. System learns from patterns over time

This is critical for compliance and trust.

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8) Build explainability into every output

Every recommendation should answer:

• What was recommended?
• Why?
• What data was used?
• What assumptions were made?
• What alternatives were considered?
• What would change the recommendation?

Example explanation:

Recommended delaying Social Security to age 70 because guaranteed lifetime income increases, portfolio withdrawals decrease in early retirement, and the household has sufficient taxable assets to bridge the gap.

For advisor routing:

Assigned to Retirement Specialist A because the household has RMDs, Roth conversion opportunities, spouse-survivor planning needs, and complexity score above the threshold.

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9) Put governance and compliance first

This kind of engine can create regulatory and suitability risk.

Governance requirements

• Model approval committee
• Assumption review cadence
• Version control for rules and models
• Audit trail for every recommendation
• Bias/fairness testing
• Exception handling process
• Escalation paths
• Documentation of limitations

If recommendations affect suitability or fiduciary advice, involve compliance early.

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10) Decide the operating model across the planning team

You need to define how the team uses the system.

Common operating model

• Central planning engine creates standardized outputs
• Specialist planners interpret and personalize advice
• Advisors own the client relationship
• Cases are routed based on complexity and specialization
• Monthly calibration meetings review overrides and outcomes

Team roles

• Product owner
• Planning SME
• Compliance lead
• Data engineer
• Quant/modeling lead
• Advisor operations lead
• Supervisor for overrides

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11) Start with a minimum viable version

Don’t try to automate everything at once.

Phase 1

• Advisor routing based on rules + scoring
• Basic retirement income rules
• Simple cash flow projection
• Manual planner approval

Phase 2

• Tax-aware withdrawal sequencing
• Social Security scenario comparison
• Monte Carlo simulation
• Capacity balancing for advisor routing

Phase 3

• Optimization across tax, income, and claims
• Personalized guardrails
• Feedback loop from planner decisions
• Continuous model tuning

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12) Build feedback loops from real outcomes

Track:

• Recommendation accepted vs overridden
• Planner satisfaction
• Client outcomes
• Revenue retention
• Retirement plan success metrics
• Advisor utilization
• Time-to-first-plan
• Compliance exceptions

Use this data to refine rules, scoring, and model assumptions.

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13) Recommended architecture

A practical architecture:

• Client data layer
  • CRM, planning software, portfolio system, document store
• Rules engine
  • Eligibility, routing, policy constraints
• Planning calculation engine
  • Cash flow, taxes, withdrawal logic, Monte Carlo
• Recommendation layer
  • Scores, ranked recommendations, explanation text
• Workflow layer
  • Review, approval, override, audit trail
• Analytics layer
  • Outcomes, calibration, capacity, QA dashboards

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14) What to document before launch

Create these documents:

• Advice policy
• Data dictionary
• Model assumptions
• Advisor routing policy
• Override policy
• Compliance review checklist
• Testing plan
• Change management process

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15) Practical next steps

If you want to move quickly, do this in order:

1. Define the decision types and business rules
2. Standardize client and advisor data
3. Build a simple rules engine
4. Add a retirement cash flow model
5. Add advisor scoring and routing
6. Add human approval and audit logs
7. Test against real historical cases
8. Roll out to a pilot group
9. Review overrides and refine
10. Expand gradually

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If you want, I can also help you with one of these next:

1. a sample decision framework
2. a data schema
3. a rules engine design
4. a scoring model for advisor recommendations
5. a roadmap to implement this in your CRM/planning stack