Nine-Step Playbook: Human-in-the-Loop Validation for AI Financial Models
Originally published:
AI-generated financial models can appear correct while encoding flawed assumptions, requiring a structured validation architecture that separates interpretation, calculation, and human approval into independently testable stages. The framework covers machine-readable assumption schemas with provenance tracking, deterministic calculation engines, four categories of independent checks, and full audit trails. Human review must be a substantive control, not a formality.
Why it matters for finance
Finance professionals adopting AI-assisted modelling need a defensible governance architecture that preserves source traceability, independent calculation integrity, and accountable approval records to meet audit and control standards.
- Define and keep distinct the five validation objects — inputs, assumptions, evidence, model outputs, and professional decisions — so no single AI response collapses all five into one answer.
- Separate AI-assisted extraction from deterministic calculation by routing structured candidate assumptions through schema and provenance validation before any human approval step.
- Require all material assumptions to be recorded as machine-readable structured records including source reference, unit, status, and approval metadata rather than as generated prose.
- Run post-approval calculations through a deterministic engine and apply four independent check categories: formula and dependency checks, accounting reconciliation checks, boundary and type checks, and regression tests against pre-verified cases.
- Test model behaviour under changing assumptions using sensitivity and scenario tests to surface directional inconsistencies, distinguishing invariant checks, expected directional checks, and regression comparisons.
- Maintain a full audit trail per calculation run capturing model version, input version, validation results, output version, reviewer identity, timestamps, and the rationale for any override.