AI anomaly detection in financials
By the time a margin problem is big enough to notice in a monthly close, it's often been compounding for weeks. This playbook runs anomaly detection across your financials continuously, catching the small variance - a 2% margin slip in your second-best unit - before it becomes a 6% one.
Establish each unit's normal range
The AI needs a baseline of typical variance per metric, per unit, before it can tell normal fluctuation from a real anomaly.
Set sensitivity per metric
Food cost % might warrant a tighter anomaly threshold than, say, day-to-day sales variance, which naturally swings more.
Route flags to the right reviewer
Anomalies should land with whoever can actually investigate - typically the unit's controller or area director, not a generic alert inbox.
Worth knowing
Anomaly detection surfaces what's unusual, not what's wrong - a real anomaly might be a one-time event like a private event booking, not an error. Treat flags as a prompt to investigate, not a verdict.