AI for Financial Advisors & RIAs

AI Fee Schedule Comparison for Advisor Prospects

How AI streamlines fee schedule comparison for prospects coming from other firms. Compliance-aware, side-by-side, decision-grade.

Fee comparison is one of the most common prospect questions: "what would I pay you vs my current advisor?" Getting that answer right — quickly, accurately, compliance-safely — converts prospects who would otherwise stall. AI compresses the workflow from hours to minutes.

Here's the operator process.

What the workflow does

For a prospect considering switching firms:

  • Captures prospect's current firm fee structure (advisory fee, fund expense ratios, platform fees, account fees)
  • Captures prospect's portfolio size and account types
  • Compares against the firm's fee structure on identical conditions
  • Produces a side-by-side, year-by-year fee comparison
  • Drafts the conversation framing for the advisor
The output is a one-page side-by-side the advisor reviews before the prospect conversation.

The inputs

For each prospect comparison, collect:

  • Current firm's advisory fee schedule (typically AUM-based tiers)
  • Account-level fees (annual maintenance, transaction, statement)
  • Platform fees (TAMP, sub-advisory)
  • Fund-level fees (ETF/mutual fund expense ratios, hidden 12b-1 fees)
  • Prospect's portfolio size and account structure
  • Time horizon (typically 10 or 20 years for the projection)
The current-firm data usually comes from the prospect's statements or from the prospect verbally describing it. Be precise — AI is only as accurate as the inputs.

The prompt scaffold

`` Compare two advisory fee structures over [TIME HORIZON] years for a prospect considering switching firms.

PROSPECT

  • Total AUM: $[X]
  • Account types: [taxable, IRA, Roth, etc. with rough allocation]
  • Expected annual return assumption: [advisor-set, typically 6-7% balanced]
CURRENT FIRM
  • Advisory fee tier: [%] on first $[X], [%] on next $[X], etc.
  • Platform/TAMP fee: [%]
  • Avg fund expense ratio: [%]
  • Account-level fees: $[X] annually
  • Other fees: [list]
OUR FIRM
  • Advisory fee tier: [%] on first $[X], [%] on next $[X], etc.
  • Platform/TAMP fee: [% or none]
  • Avg fund expense ratio: [% — use firm's typical model portfolio]
  • Account-level fees: [if any]
  • Other fees: [if any]
OUTPUT
  • Year 1 total cost: current firm vs ours (detail each component)
  • 10-year total cost difference (cumulative)
  • 20-year total cost difference (cumulative, both nominal and assuming portfolio growth)
  • Plain-English summary of the meaningful differences
  • Honest acknowledgment of where the current firm may have advantages (some clients pay more for specific services)
  • Disclosures: assumption-dependent, not a guarantee, etc.
Format as a one-page side-by-side suitable for advisor review.
`

What you get

A side-by-side with hard numbers:

` PROSPECT FEE COMPARISON — 10-YEAR HORIZON $2.5M AUM, balanced 60/40 allocation, 6.5% return assumption

YEAR 1 ALL-IN FEES Current firm: $36,250 (1.45% all-in) Our firm: $21,500 (0.86% all-in) Difference: $14,750 lower with us

10-YEAR CUMULATIVE Current firm: $462,000 Our firm: $271,000 Difference: $191,000 lower with us

20-YEAR CUMULATIVE (assuming portfolio growth) Current firm: ~$1.3M Our firm: ~$770k Difference: ~$540k lower with us

WHAT'S DIFFERENT

  • Advisory fee: 80 bps vs our 50 bps
  • Platform fee: 25 bps vs our 0 bps (we don't use a TAMP)
  • Fund expense ratio: avg 0.65% vs our 0.35% (we use lower-cost ETFs)
  • Account fees: $300/year vs $0 with us
WHERE CURRENT FIRM MAY BE A FIT
  • If they're providing specific services (e.g., concierge tax planning) that we don't offer at this fee level
  • If the relationship and trust value outweighs the fee differential
DISCLOSURES
  • Comparison based on stated current fees and our published fee schedule
  • Portfolio growth assumption is illustrative, not a guarantee
  • Actual costs depend on portfolio composition and account activity
``

Compliance considerations

Fee comparisons fall under several rules:

  • SEC Marketing Rule (Rule 206(4)-1): Any performance or fee comparison must be fair and balanced. The "where current firm may be a fit" section isn't optional — it's required to avoid creating misleading impressions.
  • Reg BI / Form CRS: Disclosure of total costs is required when recommending. Fee comparisons that omit material costs are problematic.
  • State adviser rules: Some states have specific rules about competitive comparisons.
Practical compliance for fee comparison workflow:
  • Always include disclosures
  • Always acknowledge limitations of the comparison
  • Always note assumption dependency
  • Have compliance review the prompt template and sample outputs
  • Never present comparison as if it's audited or guaranteed accurate

The mistakes to avoid

Mistake 1: Inaccurate current-firm fees. Prospects often don't know their full fee picture. Verify from statements before running the comparison.

Mistake 2: Cherry-picking favorable assumptions. If the comparison only works at 7% return and falls apart at 5%, it's not a real comparison.

Mistake 3: Skipping the "where they may be a fit" section. This isn't a politeness move; it's compliance. Omitting it creates a misleading comparison.

Mistake 4: Sending without disclosures. Fee comparison shared with the prospect must include the disclosure footer. AI defaults often skip this.

What it changes in the prospect conversation

Without this workflow:

  • Advisor: "Let me get back to you with the comparison"
  • 2-3 day delay
  • Comparison done manually, often less precise
  • Prospect cools, conversation drags
With this workflow:
  • Advisor: "Let me pull this up while we talk"
  • Comparison appears in 5-10 minutes during the meeting
  • Specific numbers anchored to the prospect's actual situation
  • Decision happens in the same meeting more often
The conversion lift on this workflow is meaningful — prospects who get specific, numbered comparisons in real-time convert 15-25% better than prospects who get follow-up comparisons.

When the comparison favors the current firm

Sometimes the prospect's current advisor is the better deal. The workflow should produce that answer honestly when true. The compliance posture and the relationship posture both require this.

The right move when the comparison doesn't favor us:

  • Present the comparison anyway
  • Identify the dimensions where we differ qualitatively (service depth, planning capability, etc.)
  • Let the prospect decide
Forced misrepresentation to "win" the prospect is regulatory exposure and reputation risk. Don't.

Bottom line

Fee comparison is one of the highest-frequency prospect questions and one of the easiest to automate well. The workflow produces compliance-grade, fair, fast comparisons that change the conversation from "let me get back to you" to "here are the numbers right now."

Done right, it converts more prospects who would otherwise stall. Done wrong, it creates compliance exposure and trust problems. The operator discipline is the difference.

Frequently asked questions

Is AI-generated fee comparison compliant?

Yes — when the comparison is fair, balanced, includes disclosures, acknowledges limitations, and notes where the current firm may be a better fit. SEC Marketing Rule and Reg BI both require fair presentation. Cherry-picked or misleading comparisons create exposure.

How accurate is AI for fee math?

AI handles arithmetic reliably given accurate inputs. The accuracy depends on the prospect's current-firm fee data — verify from statements, not just verbal recall. Garbage in, garbage out.

What if the comparison shows the current firm is cheaper?

Present it honestly. The right move is to identify qualitative differentiators (service, planning depth, relationship) rather than misrepresent the numbers. Compliance and reputation both require fair presentation.

How long does AI fee comparison take during a prospect meeting?

5-10 minutes during the meeting once the workflow is built. Versus 1-3 days for manual comparison, which often kills meeting momentum. The real-time comparison materially improves conversion.

What disclosures must AI fee comparisons include?

Assumption dependency (return rates are illustrative), data source acknowledgment (based on stated current fees), limitations (not a guarantee, actual costs vary), and a fair acknowledgment of where the current firm may be a better fit on non-fee dimensions.

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