Two years of fabricated monthly financials for a fictional Nordic manufacturer, with four scenarios hidden in the data and the correct findings written first. Three independent AI analyses found all four, with no false positives and no wrong figures across thirty findings. The strongest evidence in one scenario went unexamined.
At Paju Consumer Products, Timo in controlling is writing the commentary the night before the board meeting.
Material costs are up twelve per cent and gross margin is down three points, both numbers are right, and neither one tells him whether the cause is commodity inflation or something in the product mix.
The cause sits in the product-level detail that nobody opens before the meeting, so the obvious explanation goes into the commentary and the board moves on.
We built two years of monthly financials for a fictional Nordic consumer-goods company, and we hid four scenarios in the data.
In one, a promotional programme shifts volume into a higher-cost line. In another, a single customer’s receivables grew from about two million to nearly four and a half million euros behind a sale-and-leaseback. A third showed revenue growth in the Baltics that was bought with escalating discounts. And the fourth was a trap: a January revenue drop that is just seasonality.
Automated arithmetic raised about three hundred and thirty flags in twelve months, and all were correct. But a flag only says something moved, not why. Three independent AI analyses each went through the data on their own, blind to each other and to the answers, and had to explain what was behind those flags with evidence from the data.
All three found all four scenarios. Every run traced the cost rise to the product mix, citing flat unit costs.
Each one named the customer behind the growing receivables and traced the sale-and-leaseback that was keeping the earnings comfortable.
The Baltic growth came back in every run as bought with escalating discounts, with margin falling. And the January drop was identified as seasonality.
Across thirty findings, no false alarms worth acting on, and no cited figure was wrong.
The company’s financials are entirely invented, but the method transfers to a real month-end. Run the arithmetic, then the blind analyses, then check what comes back, and the board gets the cause behind every flag, and the evidence for it.
If you’d like to see what we could solve for you, write to us at hapax.fi.