Suitability Assessment

Suitability Assessments exist only in the heads of registered representatives. When a client asks why a particular product was recommended, the rep explains verbally. When a Reg BI examination asks for documented basis of recommendation, the branch manager discovers there is no written Suitability Assessment — just the rep's memory and a signed new account form.

None — AI cannot evaluate recommendation appropriateness because no formalized Suitability Assessment exists. There is no machine-readable record of client risk profile, investment objectives, or recommendation rationale.

Suitability Assessments are captured as free-text notes in the CRM system or on paper forms scanned to PDF. A rep types 'Client is moderate risk, recommended XYZ fund because it matches her goals' into a notes field. The Suitability Assessment record exists but lacks consistent structure — risk tolerance might be 'moderate,' 'medium,' '3 out of 5,' or 'not too aggressive' depending on who wrote it.

AI could potentially read Suitability Assessment notes, but cannot reliably extract client risk profile parameters, compare them against product characteristics, or validate recommendation logic due to inconsistent terminology.

Suitability Assessments follow a standardized template with consistent fields: client risk tolerance (1-5 scale), investment objective (growth/income/preservation/speculation), time horizon (short/medium/long), liquidity needs, concentration limits, and a structured rationale section. Reps complete the template for each recommendation. The Suitability Assessment is stored as a completed form in the client file, but product characteristics are referenced by name rather than linked to a product database.

AI can extract and compare Suitability Assessment fields across clients, identify patterns in recommendation rationale, and flag assessments where stated risk tolerance conflicts with stated objectives. Product-to-client matching requires manual lookup.

Suitability Assessments are stored as structured database records with discrete fields linked to the client master and product master. Each Suitability Assessment links the client's risk profile (formalized as structured attributes) to a specific product's characteristics (risk rating, fee structure, liquidity terms, minimum holding period). The recommendation rationale is captured in a structured format referencing specific Form CRS disclosure items and Reg BI best interest factors. A compliance query can return 'all Suitability Assessments where client risk tolerance is below 3 and the recommended product risk rating exceeds 7.'

AI can perform automated Reg BI suitability screening across all Suitability Assessment records, flag mismatches between client profiles and product risk characteristics, and generate exception reports for supervisory review.

The Suitability Assessment is a schema-driven compliance entity with explicit relationships to the client's complete financial profile — net worth, income, existing holdings, tax status, beneficiary designations — and the product's full characteristics from the product master. Each assessment carries machine-readable references to applicable Reg BI care obligation factors, Form CRS cost disclosures, and reasonably available alternatives that were considered and rejected. An AI agent can ask 'for this Suitability Assessment, what were the three lowest-cost alternatives and why were they not recommended?' and receive a structured, auditable answer.

AI can perform fully automated Reg BI best interest evaluations on each Suitability Assessment, including reasonably available alternatives analysis, cost comparison, and conflict of interest disclosure validation without human pre-screening.

The Suitability Assessment is a living compliance document that auto-generates from real-time client profile data, current product characteristics, live market conditions, and updated regulatory requirements. When a client's risk profile changes due to a life event, all outstanding Suitability Assessments are re-evaluated automatically. When a product's risk rating changes, affected assessments are flagged. The assessment dynamically references current Reg BI interpretive guidance and SEC staff bulletins. Reps validate and add judgment-based rationale to a pre-populated, continuously current assessment rather than building one from scratch.

Fully autonomous suitability intelligence. AI can generate, validate, and monitor Suitability Assessments across the entire book of business, proactively identifying assessments that have become stale due to changed client circumstances, product characteristics, or regulatory requirements.

Ceiling of the CMC framework for this dimension.