Robo-Advisor Edge-Case Inventory Worksheet
A robo-advisor that handles the median client well is unremarkable; one that handles the lifecycle inflections — retirement, divorce, large deposit, panic-sell — is defensible. This worksheet enumerates the lifecycle events the firm's robo-advisor must respond to, the data signals each requires, and the synthetic households that produce them. The output goes to engineering as a corpus spec and to compliance as evidence of design rigor.
What you walk away with
~18 min · 4 sections · 6 fields- An enumerated lifecycle-event list with required data signals captured per event.
- A documented expected algorithmic response per event.
- A named archetype set that produces each event in the synthetic corpus.
- An entry into the Robo-Advisor Defensibility Assessment for the firm's overall posture.
Robo-advisor scope
Lifecycle events
List each lifecycle event the robo-advisor must respond to, the data signals required to detect it, and the expected algorithmic response.
What inputs trigger the event in the system?
What the robo-advisor should do — allocation change, KYC refresh, advisor escalation.
Reference an existing archetype or note the gap.
What inputs trigger the event in the system?
What the robo-advisor should do — allocation change, KYC refresh, advisor escalation.
Reference an existing archetype or note the gap.
What inputs trigger the event in the system?
What the robo-advisor should do — allocation change, KYC refresh, advisor escalation.
Reference an existing archetype or note the gap.
What inputs trigger the event in the system?
What the robo-advisor should do — allocation change, KYC refresh, advisor escalation.
Reference an existing archetype or note the gap.
What inputs trigger the event in the system?
What the robo-advisor should do — allocation change, KYC refresh, advisor escalation.
Reference an existing archetype or note the gap.
Oversight & escalation
When the algorithm flags an event, how does it route to a human?
Coverage roll-up
Out of 6. Below 3 indicates a structural oversight gap.
Aggregate of event coverage (70%) + oversight (30%). Below 50% is pre-launch risk; above 80% is defensible.
Next steps
The populated worksheet routes directly into the Robo-Advisor Pre-Launch Defensibility Assessment. For each event with status < 'verified in CI', map the archetype gap to the Robo-Advisor Pre-Launch Data Coverage checklist.
Key takeaways
- Panic-sell is the most under-modeled event. Most platforms detect it after the fact; the algorithmic response is poorly defined.
- Retirement transition is detectable from cash-flow signals before the client self-reports it. Designing for that is what makes a defensible robo-advisor.
- Beneficiary changes following life events (marriage, divorce, death, birth) often don't propagate through linked accounts. That's a structural bug class.
- KYC refresh on material change is increasingly an exam focus. Define the trigger, log the prompt, log the response.
FAQ
Should we list every possible event?
Start with the events that materially change allocation or trigger a regulatory obligation. The behavioral coaching pack contains a longer list of behavioral signals worth iterating against.
What if our platform is advisor-led?
Same exercise. The events become advisor prompts rather than algorithmic actions, but the data signals and the synthetic archetypes are identical.