Free Lesson

Run 3 checks on any AI finance report

Hosted by Albert Lee

Wed, Sep 16, 2026

1:00 PM UTC (30 minutes)

Virtual (Zoom)

Free to join

Invite your network

What you'll learn

Diagnose a variance before you explain it

Uniform segment misses, mismatched allocation bases, unlabelled assumptions — seen live in a real $255M variance.

Run a 3-check review in under 20 minutes

Completeness, consistency, assumptions — a portable sequence for any dashboard, variance pack or model.

Apply a release gate before your name goes on it

Use the Explainability Test to decide if output is decision-ready — and find which layer your process stops at.

Why this topic matters

A dashboard I built reported a $255M revenue miss and recommended a CFO-led recovery session. I had already debugged it once. Every tie-out tied. Months later I found completeness had never been established. AI doesn't reduce the need for financial judgment — it raises the stakes on it. Everyone can generate the report now. The person who can tell you whether to trust it is the scarce resource.

You'll learn from

Albert Lee

HKICPA Fellow · ex-PwC · Founder, Axiom FP&A · AI Finance Coach

I review 5–10 AI-built finance projects every week as an AI Finance Coach at AI Finance Club, and have personally coached around 200 senior finance professionals. I see the same failure patterns constantly: output that ties perfectly, looks finished, and hasn't earned the conclusions it's printing.

Before that I spent a decade owning the numbers myself — starting in PwC Assurance auditing listed groups in Hong Kong and mainland China, then as FP&A Manager, APAC for a US$550M consumer brand, reporting to the CFO. I've also delivered corporate training for Allianz Global Investors.

If your name goes on the numbers, this one's for you.

See all products from Albert

Sign up to join this lesson

By continuing, you agree to Maven's Terms and Privacy Policy.