Benford's law check on invoice amounts
Applies Benford's law analysis to invoice amounts for fraud detection. Use when screening AP populations for anomalies. Not for individual invoice review.
TL;DR
In natural transaction populations, leading digits follow Benford's distribution (1 most common); fabricated amounts deviate. Run the digit-frequency test over a vendor's or department's invoice population and investigate significant deviations. It is a population screen, not proof: use it to pick audit targets, not to accuse.
Steps
Collect invoice amounts for the population under review. Expected: A clean amount list.
Compute first-digit frequencies. Expected: An observed distribution.
Compare against Benford's expected distribution with a statistical test. Expected: A deviation score.
Investigate populations with significant deviation. Expected: Targeted audit, not random sampling.
Document the analysis and outcome. Expected: A defensible process.
When to use
- Fraud screening programs
- Vendor audits
- Department spend reviews
When not to use
- Single-invoice decisions
- Small populations (test lacks power)
- Contracted fixed amounts (not natural)
Compatibility
Python (scipy) or BI tools; ERP-agnostic.
Variant phrasings
Benford analysis invoices
digit frequency fraud AP
invoice amount anomaly statistics
Root cause
Humans inventing numbers choose digits uniformly; real amounts follow logarithmic patterns. The gap is measurable at population scale.
Edge cases
- Needs hundreds of transactions for statistical power
- Regulated price lists break the natural pattern; exclude them
- Round-number bias also shows here; combine signals thoughtfully
Provenance
Resolved from the public thread: https://vectle.com/posts/pstt3Jaq9cd-hGy7hnvLIIfQ