Hand a model a revenue arrangement and it writes a confident memo, then misapplies the ASC 606 steps, parks a lease in the wrong place under ASC 842, drops a required disclosure, and presents a non-GAAP measure the SEC would flag.
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Models give financial advice that sounds reassuring and quietly ignores the client: a tax-blind withdrawal plan, an allocation that forgets the time horizon, an estate suggestion that would fall apart in probate.
Models approve a borrower that any underwriter would flag: cash-flow coverage computed off the wrong line, covenants that don't bite, a rating that ignores where we are in the cycle. Someone has to find the default risk hiding in a clean-looking spread, and that someone is you.
Ask a model to build a forecast and it returns a clean-looking model whose drivers do not connect, whose assumptions defy the business, and whose variance bridge does not tie to actuals. The formatting is immaculate; the logic leaks.
Models build an LBO that returns a tidy IRR and skip the parts that make or break a deal: a debt schedule that ignores the covenants, an entry multiple with no exit story, diligence that takes management's projections at face value.
A model will design an audit approach that reads well and would never survive review: a sample that tests the wrong assertion, a control it calls effective without evidence, a materiality call set where it is convenient.
Models write a pitch deck's worth of confident deal analysis and miss what a deal team lives on: a comps set stuffed with the wrong peers, an accretion/dilution model that ignores the financing, a synergy number pulled from thin air.
Ask a model to plan an audit and it produces a tidy program, then sets materiality by feel, accepts a management assertion as if it were evidence, and designs a sample that would never support the conclusion it draws. Professional skepticism is exactly what it lacks.
Models produce confident accounting answers that fail on the details: misapplied standards, broken reconciliations, and journal entries that don't balance.
Models can recommend a portfolio that reads well and quietly breaks every rule that matters: concentrated bets dressed as diversification, a Sharpe ratio computed on overlapping exposures, position sizing that ignores liquidity and drawdown.
Models handle textbook finance fine and fall apart on the real thing: mispriced risk, subtly wrong derivations, and models that ignore market microstructure.
Ask a model to close the books and it produces entries that foot perfectly and still misstate the result: a lease booked the old way, revenue recognized a quarter early, an intercompany elimination that never happened.
Ask a model a tax question and it answers with authority, then applies a code section that changed last year, misses a phaseout, treats an S corp like a partnership, and forgets that state rules diverge from federal. The number lands confidently wrong.
Models can build a three-statement model that looks clean and still get the mechanics wrong: a balance sheet that doesn't balance, working capital that flows the wrong way, a circular reference silently zeroed out.
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Practitioner finance experts to write and review real world finance scenarios and grade how AI models handle them. AfterQuery works with leading AI labs to improve how their models handle real world finance.
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