Fee benchmarking sounds simple until you actually try to compare two clients' all-in costs. One pays 90 bps to a single RIA who manages everything. Another pays 60 bps to a wirehouse plus 25 bps in fund expense ratios plus 8 bps in transaction costs plus a 4% tax drag — and the comparison isn't a single number, it's a stack with five layers, each with its own benchmark, each negotiable on a different time horizon. The Fiduciary Fee Benchmark Dataset is 130 affluent and HNW households built for the firms that need to do this comparison rigorously: cost-transparency dashboards, DOL fiduciary documentation, fee benchmarking tools, and the platform vendors who serve them.
Fee transparency is regulated by the DOL fiduciary rule, but the data needed to deliver it honestly doesn't exist in most firms' production systems. Advisory fees live in the CRM. Fund expense ratios live in the custodian's data feed. Transaction costs are buried in trade tickets. Tax drag is an estimate that requires running the portfolio against the client's tax bracket. Stitching these together into a single all-in-cost number is a data-engineering project most firms simply skip — and clients then get fee disclosures that look low because they only count the visible layer.
For compliance teams, this means the fee disclosure your firm hands to a regulator may not survive a careful examiner review. For platform vendors building cost-transparency tools, it means you can't demo the product on prospect data because the prospect's data isn't structured the way your tool needs.
This Data Set provides 130 households where every fee layer is structured: advisory fees in basis points with tier breakpoints; fund expense ratios attached to each holding; transaction-cost estimates per account; and tax-drag estimates pre-computed against the household's marginal bracket. It's the canonical fixture for any tool that needs to demonstrate or test fee transparency rigorously.
Validates that the firm's annual fee disclosure document includes all-in cost, not just advisory fees, by running the disclosure generator against this Data Set's pre-computed total-cost-of-ownership values and reconciling against the line-by-line detail.
Demos cost benchmarking to RIA prospects using realistic households spanning $1M to $30M, so the prospect sees exactly what their clients' fee comparisons would look like — without needing to share real client data first.
Tests fee allocation logic across multi-firm engagements (separately managed accounts at three firms, plus DAF, plus private equity custody) to validate that the family office's all-in TCO calculation reconciles before quarterly client reporting.
Walks broker-dealer compliance teams through the fee disclosure obligations using realistic household examples, demonstrating where naive fee-disclosure logic understates true cost and where examiners are likely to focus.
Evaluates target-firm fee structures during diligence using a calibrated benchmark — does the target's average client fee fall within or outside the benchmark range for the wealth tier they serve?
The 130 households are drawn from six archetypes spanning peak earners ($1M–$3M wealth tier) through ultra-high-net-worth ($10M+). The mix is deliberately weighted toward the wealth tiers where fee complexity matters most — about 50% in the $1M–$5M range where tiered breakpoints kick in, 35% in the $5M–$30M range where multi-firm arrangements become common, and 15% above $30M where family-office and percentage-of-net-worth structures appear.
Every household has a complete fee structure: advisory fee in basis points (with tier breakpoints if applicable); fund expense ratios attached to each underlying holding; transaction-cost estimates derived from trade frequency and average ticket size; and a tax-drag estimate computed against the household's federal+state marginal bracket. Where the household has a multi-firm engagement, each firm's slice is structured separately, with explicit attribution of which assets sit where. Performance-based fee arrangements (where present) are structured with hurdle rates, high-water marks, and crystallization frequency.
The Data Set ships as JSON, CSV, and Parquet. The WealthSynth Methodology PDF documents the fee taxonomy, the methodology for tax-drag estimation, the calibration source for typical fee ranges by wealth tier (a blend of Cerulli, Tiburon, and Kitces survey data), and the expected use cases for each format.
A redacted summary of one household from this Data Set — names, employers, exact balances, and metro area are stripped. Ages are bucketed, income and net worth are reported as bands. The full record (and all 130 like it) ships in the ZIP.
{
"fees.advisory_fee_bps": <value>,
"fees.tier_breakpoints[]": <value>,
"fees.fund_expense_ratios": <value>,
"fees.tax_drag_estimate": <value>,
"fees.total_cost_of_ownership": <value>
}Returns each household's total cost as a single basis-point number summing advisory, fund expense, transaction, and tax-drag layers. The canonical TCO calculation for fee benchmarking.
households.map(h => ({
id: h.id,
total_bps: h.fees.advisory_fee_bps +
h.fees.weighted_avg_fund_er_bps +
h.fees.transaction_cost_bps +
h.fees.tax_drag_bps
}))Surfaces households whose AUM is within 10% of a tier breakpoint where the fee schedule drops — a coaching opportunity for advisors and a benchmark check for fee-transparency tools.
households.flatMap(h =>
h.fees.tier_breakpoints
.filter(t => h.assets.aum >= t.threshold * 0.9 &&
h.assets.aum < t.threshold)
.map(t => ({ household: h.id, breakpoint: t }))
)Returns households where tax drag exceeds advisory fee — a signal that tax-loss harvesting or asset-location optimization would compound more value than a fee renegotiation.
households.filter(h => h.fees.tax_drag_bps > h.fees.advisory_fee_bps )
Computes the median and 90th-percentile all-in cost for each wealth tier band, producing the benchmark distributions that a cost-transparency tool can display to clients.
groupBy(households, h => h.demographics.wealth_tier)
.map((tier, hs) => ({
tier,
median_bps: median(hs.map(h => h.fees.total_cost_of_ownership)),
p90_bps: percentile(
hs.map(h => h.fees.total_cost_of_ownership), 90)
}))Each household's fee structure is generated against archetype-specific distributions of advisory fee schedules, fund mix, and trading activity. Advisory fee tiers are calibrated against published Cerulli wealth-tier benchmarks; fund expense ratios are sampled from the actual Morningstar distribution for the holding categories represented; transaction costs use a regulated-broker estimate of $0.005 per share for equities plus structured estimates for fixed-income and alternatives. Tax-drag is computed from the household's marginal federal+state bracket multiplied by an asset-mix-aware turnover assumption. Households with performance-based fees apply Cerulli-typical hurdle and high-water-mark structures. The full corpus passes the WealthSynth consistency validator (fee math reconciles end-to-end and tier breakpoints are consistent with disclosed schedules) and the LLM-as-judge gate. Annual refresh re-runs against current-year fee benchmarks and the prevailing fund expense distribution.
The advisory-fee tier ranges are calibrated against Cerulli Associates' wealth-tier benchmarks. Fund expense ratios use the actual Morningstar distribution for the holding categories. Transaction costs follow standard broker-dealer estimates. The Methodology PDF includes a citation appendix for every benchmark used.
Use of the Data Set to derive published benchmarks (industry averages, percentile ranges) is permitted for internal research and client-facing benchmarks specific to your firm. Republishing aggregated benchmarks for sale to third parties is restricted; reach out about a research-use license if that's the use case.
Realistic. Fees vary on the same calibration distributions you'd see in a Cerulli or Tiburon survey — about 65% of advisory fees fall in the 65–110 bp range, with declining tiers above $5M. Round-number fees (a flat 100 bps across all assets) are present but not over-represented, since they're not the modal real-world structure.
Tax drag is computed as the household's effective marginal tax rate on investment income (federal + state) multiplied by an asset-mix-weighted turnover estimate (~15% for typical balanced portfolios, higher for active equity, lower for ETF-heavy mixes). It's a forward-looking estimate of annual tax cost, not a backward-looking realization. The methodology matches the SEC's preferred fiduciary-disclosure approach.
Yes — about 12% of the corpus has at least one performance-based fee arrangement, typically on hedge fund or private-credit allocations within the broader portfolio. These structures include hurdle rates, high-water marks, and crystallization frequency in the structured fee data.
About 8% of the corpus uses flat-dollar advisory fees (typical of XY Planning Network–style firms or family-office percentage-of-net-worth structures). These are stored in `fees.advisory_fee_dollar` with the corresponding implied bps for benchmark comparison.
Yes — workplace retirement-account fees (recordkeeping, plan-level expenses) and IRA platform fees are structured separately from advisory fees, since DOL fiduciary disclosure treats them differently. The Methodology PDF documents the field-by-field structure.
B01 focuses on suitability red flags (concentration, age, cognitive markers) for broker-dealer supervisory testing. B07 focuses on fee structures for fiduciary-rule and cost-transparency use cases. Different regulatory frame, different population focus. Many compliance buyers purchase both to cover the full DOL/Reg BI/FINRA review set.
130 synthetic households tuned for Reg BI suitability testing — concentrated holdings, age 75+, recent inheritance, cognitive decline markers, and risk-mismatch flags. Each record carries the eligibility triggers required to exercise broker-dealer supervisory workflows end to end.
400 prospect households covering RIA client variety from formation through retirement. KYC-complete records, goal-based planning fields, initial recommendation outputs, and CRM-compatible field naming. The broadest single bundle by archetype coverage.
110 HNW and UHNW households with estate planning readiness scores, trust structures, gifting histories, charitable giving data, and GST exemption tracking. Complements B09 (Next-Gen Attrition) and B12 (Estate & Trust Planning).
Purchases are for internal use only. Redistribution or resale of data is prohibited under the WealthSchema Data License.
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