Editing AI for Wealth Management (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ Wealth Management AI Impact
! Application !! Client Benefit !! Fee Level !! Evidence
|-
| Robo-advisor (standard) || Low-cost diversification || 0.25-0.50%/yr || Strong (passive beats most active)
|-
| Tax-loss harvesting (ETF) || +0.5-1.0%/yr after-tax || 0.25% (included) || Moderate (simulation-based)
|-
| Direct indexing TLH || +1-2%/yr after-tax || 0.30-0.40%/yr || Strong for high earners
|-
| Behavioral nudges || Reduced panic selling || Included || Moderate (behavioral studies)
|-
| Conversational planning || Access for mass market || Low or free || Emerging
|}

'''Failure modes''': Overconfidence in optimization — mean-variance optimization is highly sensitive to expected return estimates which are notoriously difficult to forecast. All-ETF portfolios during market stress — robo-advisor clients often see similar drawdowns to the market. Tax-loss harvesting wash-sale violations — accidentally triggering IRS wash-sale rules. Behavioral homogeneity — millions of clients using same algorithm creating correlated behavior during stress. LLM hallucination in financial advice — incorrect financial information with high confidence.
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