Financial model review with AI, without leaking the crown jewels

A short coffee version.

I do not ask AI to “bless” a model. I use it like a sharp intern who never gets tired. It traces links, explains jumps, and points me to where my judgment should go first. The win is faster triage and cleaner fixes, not blind trust.

How I run the review

I give context in plain language, a map of sheets, and the outcomes I need. The assistant follows drivers into revenue, opex, working capital, capex, and the three statements, then checks invariants like the balance sheet equation and the cash roll.

It rebuilds a few rows to spot sign mistakes, broken links, circularity, and odd spikes. It writes a one-page narrative with a fix list that names sheets and cells. I validate each claim by reproducing the path and testing before and after on EBITDA, cash, covenants, and key ratios. If something moves without a clear cause, it goes back.

Confidentiality without slowing down

First, I confirm the allowed environment. If there is a private or zero-retention setup, I use it with logging and encrypted storage. If not, I keep raw values out and share structure instead. I provide sheet names, ranges, formulas, and an integrity-checks tab while masking numbers or using a small synthetic twin that preserves logic and magnitude.

For highly sensitive files, I review inside a locked desktop where clipboard, files, and exports are controlled. I keep an audit note of what was shared and why, and I get written approval under the NDA that specifies scope and tooling.

Risks and the simple counters

Leakage is handled by environment controls, masking, synthetic data, and a habit of sharing wiring over values. Model drift is handled by working on a copy, logging changes, and reconciling headline outputs after each fix. False confidence is handled by pushing for cell-level traceability and recreating small sections by hand. Trust arrives with proof.

Why this works

Reviews surface issues in clusters, new joiners learn by following breadcrumbs, leaders get a tighter story, and confidentiality stays intact. Every pass leaves the model cleaner and the narrative clearer. That is continuous improvement in real life.