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Benford's Law Fraud Check
x402
Benford's Law Fraud Check is a paid API for AI agents from bot-bazaar.fly.dev, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-31).
Tests a series of ledger figures against Benford's Law expected leading-digit frequency to detect anomalies indicative of fraud or data manipulation.
Price / call$0.01
Success—
StatusUnknown
How this service works
Test a series of ledger figures against Benford's Law expected leading-digit frequency (30.1% for digit 1).
Output
Returns an analysis of the leading-digit frequency distribution of the submitted values compared against Benford's Law's expected probabilities (e.g. ~30.1% for digit 1), flagging whether the distribution deviates significantly in a way that suggests data manipulation or fraud.
Example use cases
Expense report anomaly detection
“I have a set of expense report amounts submitted by employees this quarter — can you check them against Benford's Law to see if any look like they might have been fabricated? The values are: 245, 1832, 399, 4910, 1200, 780, 3300, 550, 2100, 990.”
Auditing general ledger entries
“We're doing an internal audit and I need to run our general ledger figures through a Benford's Law check to flag any statistical anomalies — here are the amounts: 5800, 1200, 3400, 990, 7100, 2250, 4600, 880, 1750, 6300.”
Detecting manipulated financial data
“Before I submit this financial report, can you test these figures against Benford's Law to make sure the leading-digit distribution doesn't raise red flags: 12000, 3500, 48000, 7800, 15000, 29000, 6700, 31000, 8900, 10500?”
Technical details
Endpoint
GEThttps://bot-bazaar.fly.dev/v1/x402/benfords-law-fraud-check?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need a quick, inexpensive statistical fraud screen on a set of financial or ledger figures using Benford's Law. It is ideal for forensic accounting pre-checks, auditing submitted expense or transaction data, or any scenario where you want to flag numerically suspicious datasets before deeper investigation. Prefer this over manual analysis or full audit tools when speed and cost matter and you have a list of numeric values ready to test.
Side effects
- Missing or empty 'values' query parameter returns an error
- Non-numeric values in the comma-separated list may cause parsing errors
- Too few values provided may yield statistically unreliable results
- Payment of $0.01 USDC not completed results in HTTP 402 response
- Malformed query string causes bad request error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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