AI Financial Planning Software Quiz

Answers and explanations appear as you take the quiz.

1. According to the guide, which layer of an advisor planning platform relies mainly on a decades-old statistical method rather than newer AI?
   • Document extraction from PDF statements
   • LLM drafting of plan summary letters
   • The Monte Carlo projection engine
   • Meeting transcription and CRM notes
2. A plan shows an 82% probability of success. What does that number represent?
   • The share of simulated trials in which the client's money lasted
   • A forecast that markets will return 82% of their historical average
   • The advisor's confidence that the client will follow the plan
   • The portion of the portfolio held in low-risk assets
3. Why do well-built extraction pipelines send low-confidence fields to a human review queue?
   • Regulators forbid storing any value read by OCR
   • To stop uncertain values from flowing into the plan unchecked
   • Because aggregation feeds cannot read PDFs at all
   • To train the Monte Carlo engine on new data
4. What design does the guide recommend for keeping numbers in LLM-drafted plan summaries accurate?
   • Let the model recalculate projections so it can double-check the engine
   • Use a larger model that makes fewer arithmetic mistakes
   • Remove all numbers from client summaries
   • Pass the engine's outputs as structured input and automatically check that the draft's numbers match them
5. What task is Holistiplan known for, based on the guide?
   • Reading clients' tax returns to surface planning opportunities
   • Running historical bootstrapped Monte Carlo simulations
   • Transcribing client meetings into CRM notes
   • Aggregating brokerage accounts through data feeds
6. How does historical bootstrapping differ from sampling returns from an assumed distribution?
   • It uses only the single best historical decade
   • It resamples actual past return sequences, which keeps real-world extremes
   • It removes sequence-of-returns risk from the plan
   • It relies on the LLM to invent plausible return paths
7. Which practice matches how the guide describes responsible use of an AI meeting assistant?
   • Letting it save notes to the CRM automatically with no review
   • Recording meetings without telling the client, to keep things natural
   • Getting client consent and having the advisor edit the draft notes before saving
   • Using it only for internal meetings, never for client meetings
8. Why does the guide warn against treating probability of success as a forecast?
   • It is calculated from only one market path
   • It can shift a lot with small changes to return, inflation or volatility assumptions
   • Regulators require it to be reported as a range
   • LLMs generate it from text, not calculations