Competitive analysis
AuraScore 79/100

WealthTech Advisory Pricing and Investment Offering Teardown Checklist

Deconstruct digital wealth management fee structures, portfolio customisation capabilities, and onboarding thresholds.

Deploy this checklist when analyzing competing robo-advisors and hybrid wealth managers to evaluate fee transparency, asset class coverage, and portfolio rebalancing features. It equips wealth intelligence teams to spot uncaptured advisory niches.

Template

Role: Senior Wealth Management Competitive Intelligence Analyst specializing in hybrid advisory and robo-investment platforms.

Context

  • Sponsoring Wealth Firm: {{wealth_firm_name}}
  • Target Investor Wealth Band: {{client_net_worth_tier}}
  • Competing Platforms: {{peer_wealth_managers}}
  • Baseline Advisory Model: {{advisory_fee_structure}}
  • Asset Classes in Scope: {{asset_classes_covered}}
  • Review Timeframe: {{benchmark_period}}

Task

Generate a detailed competitive evaluation checklist systematically comparing advisory fee tiers, automated asset allocation techniques, tax-loss harvesting features, and onboarding minimums between {{wealth_firm_name}} and {{peer_wealth_managers}}.

Method

  1. Define mandatory checklist parameters for evaluating transparent vs. bundled management expense ratios (AUM fees, wrap fees, and underlying ETF expense ratios).
  2. Construct review items examining minimum investment thresholds, account opening deposit requirements, and fractional share execution capabilities.
  3. Establish assessment checkpoints for asset class breadth spanning {{asset_classes_covered}}, direct indexing alternatives, and private credit access.
  4. Design comparative criteria for automated portfolio algorithms, assessing drift thresholds, rebalancing cadence, and tax-loss harvesting efficiency.
  5. Audit human-advisor access models (pure digital vs. dedicated CFP access tiers) across {{peer_wealth_managers}}.
  6. Formulate testing checkpoints for client financial goal simulation, Monte Carlo stress testing, and retirement drawdown modeling.
  7. Summarize fee-to-value anomalies observed across {{client_net_worth_tier}} accounts during {{benchmark_period}}.

Constraints

  • MUST express pricing comparisons as exact basis points (bps) or transparent fixed-fee brackets.
  • MUST NOT provide generic investment advice; focus strictly on platform and feature benchmarking.
  • Ensure each checklist criterion contains a clear verification method and evidence source.
  • Output must focus specifically on {{client_net_worth_tier}} market expectations.

Output format

  • Comparative Pricing & Fee Structure Checklist (10 line items evaluating explicit, implicit, and custodial fees)
  • Asset & Portfolio Management Capability Checklist (8-10 line items evaluating rebalancing, indexing, and customization)
  • Digital Tooling & Advisory Support Checklist (6-8 line items evaluating planning engines and human advisor access)
  • Actionable Gap Matrix (Categorized by 'Immediate Win', 'Feature Parity', and 'Strategic Differentiation')

Self-review

  1. Verify that all asset categories listed in {{asset_classes_covered}} appear in the portfolio evaluation items.
  2. Check that the fee structures of {{peer_wealth_managers}} are challenged against {{advisory_fee_structure}}.
  3. Confirm that the checklist items specifically address the expectations of {{client_net_worth_tier}}.
AuraScore breakdown
79/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering10/12 · Adequate

Hard boundaries — what the model must and must not do.

Output specification6/14 · Thin

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency5/10 · Thin

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness3/5 · Adequate

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

research-analysis
research-competitive
financial-services
wealthtech
investing
pricing