> ## Documentation Index
> Fetch the complete documentation index at: https://docs.fincelo.app/llms.txt
> Use this file to discover all available pages before exploring further.

# Revenue Anomaly Agent

> How the Revenue Anomaly Agent detects statistical deviations using Z-score.

### ANOMALY AGENT — Q\&A

***

**Q: How does the Revenue Anomaly Agent detect anomalies?**

Uses a statistical baseline approach (Z-score). For each metric
(MRR, invoice count, payment amount, credit note volume), it maintains a
rolling average and standard deviation over the last 12 periods.

Any observation more than 2 standard deviations from the mean is flagged.

Examples of what it catches:

* MRR spike from a duplicate subscription being activated
* Unusual credit note volume suggesting billing errors
* Payment receipts far higher than invoiced (over-payment from customer)
* Journal entries that are outliers vs historical amounts

***

**Q: What is the Z-score rolling baseline?**

Level 1 enhancement. The Anomaly Agent now uses a rolling 12-period Z-score
rather than a fixed threshold. Threshold automatically adjusts as the business
grows. A company at ₹5Cr ARR and a company at ₹50Cr ARR have very different
normal ranges — the Z-score baseline adapts to each.

***