How anomaly-detection AI works

1. Data ingestion: Pulls millions of GL lines or AP invoices.

2. Feature engineering: Creates thousands of statistical & relational features (e.g., Benford scores, employee-vendor matches).

3. ML & rules engine: Unsupervised clustering plus business-rule overlays catch both novel and known risks.

4. Risk scoring & workflow: Items above threshold route to accountants for review.

Implementation tips

• Start small by importing the last two to four years of accounts data.

• Embed in workflow so accountants review in the same UI.

• Iterate monthly; models self-learn as new risks emerge.

• [Optional] Tune thresholds with historical anomaly or outlier cases.

Value metrics

• % high-risk items auto-cleared vs. false positives

• Manual testing hours eliminated

• Detected dollar value and number of accounts of misstatements

• Reduced internal audit costs

FAQs

1. Will AI replace auditors?
   No; it augments them by prioritizing risky items.

2. How long to deploy?
   Most teams see first results within two weeks after data connection.

3. Can I customize rules?
   Yes. AuditFlow’s rule builder supports custom thresholds and regex check