Upload a CSV, pick your amount column, and get the forensic-accounting standard screen — observed vs expected first digits, chi-square and Nigrini MAD conformity, and the exact digits that deviate. A screening signal for where to look, honestly framed. Free.
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Extracting first digits and testing against Benford's Law...
Sent to — observed vs expected digit distribution, chi-square and Nigrini MAD conformity, per-digit deviation flags, R code, and AI insights.
Analyze another fileThe analysis extracts the first significant digit of every usable amount (absolute values are used, zeros and blanks are dropped, and 0.0042 counts as a 4), then compares the observed digit shares with the Benford expectation log10(1 + 1/d). Conformity is scored two ways: a chi-square goodness-of-fit test, and the mean absolute deviation (MAD) read against Nigrini's published bands — below 0.006 close conformity, below 0.012 acceptable, below 0.015 marginal, 0.015 and above nonconformity. Per-digit standardized residuals flag which digits deviate beyond 1.96 standard errors, and a first-two-digit test runs when at least 1,000 values are available. The verdict is conforms or deviates (investigate), with a computed warning when the data's range makes Benford inapplicable.
Use it to triage transaction amounts, expenses, invoice totals, or any organically generated financial amounts before deciding where to audit.
Not for assigned numbers (IDs, phone numbers), prices drawn from a fixed list, or amounts confined to a narrow band — Benford's Law does not describe those, and deviation there means nothing. Never treat its output as proof of fraud.
Built for: Auditors, controllers, and analysts screening financial records for where to look closer
Typical data source: A ledger, expense report, or transaction export with an amount column
Any table with a column of amounts. For example, an expense ledger:
Minimum 100 rows · Best with 1,000-100,000 amounts spanning several orders of magnitude
Standard-library analysis: screen a column of amounts against Benford's Law, the forensic-accounting standard for spotting numbers that may have been fabricated or manipulated. The observed first-digit distribution is compared with the expected log10(1 + 1/d) curve using a chi-square goodness-of-fit test, the mean absolute deviation (MAD) read against Nigrini's conformity bands, per-digit standardized residuals flagging which digits deviate most, and a first-two-digit test when at least 1,000 values are available. Deviation is reported as a screening signal to investigate — never as proof of fraud — and a computed applicability check warns when the data's range is too narrow for Benford's Law to apply naturally.
Observed share of each first digit against the Benford curve — conforming data tracks the downward slope from digit 1 to digit 9.
Each digit's standardized residual with a deviates-high / deviates-low flag, so you know exactly which digits to investigate.
The itemized preprocessing ledger — what was dropped, what was kept, and why.
Nigrini's conformity thresholds with the band this data landed in, plus the applicability caveats that keep the screen honest.
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
Do these numbers look fabricated or manipulated?
Map your amount column. The screen compares its first digits with Benford's Law, scores conformity with Nigrini's MAD bands and a chi-square test, and flags the exact digits that deviate — an honest screening read that tells you where to investigate, not an accusation.
See our FAQ for details on pricing, data privacy, and how the analysis works. Every report includes a Methodology section showing the statistical test, assumptions checked, and diagnostics run.
Run any analysis on your own data — validated R analyses, interactive reports, AI insights, and PDF export.
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