FRAUD GROUPS REFERENCE
Fraud & Scam Detection Fraud Groups
Fraud & Scam Detection organizes detection into 4 fraud groups covering 200 indicators across 39 languages.
All Fraud Groups
Each fraud group contains a set of specific indicators that the API can detect. Click a group to expand its signal list. Signal lists are available to customers - sign in to view them.
01
Impersonation
impersonation
50
50 signals - request access
for the complete list.
View 50 indicators
Impersonation
impersonation
The complete signal list is available to customers. Request access to view it.
02
Payment Diversion
payment_diversion
50
50 signals - request access
for the complete list.
View 50 indicators
Payment Diversion
payment_diversion
The complete signal list is available to customers. Request access to view it.
03
Fake Invoice
fake_invoice
50
50 signals - request access
for the complete list.
View 50 indicators
Fake Invoice
fake_invoice
The complete signal list is available to customers. Request access to view it.
04
Social Engineering
social_engineering
50
50 signals - request access
for the complete list.
View 50 indicators
Social Engineering
social_engineering
The complete signal list is available to customers. Request access to view it.
Reading Fraud & Scam Detection output
What you can safely infer from a result - and what you must not.
Intended task
Flag documented fraud and scam patterns - payment diversion, impersonation, advance-fee lures - in text before money or credentials move.
Input requirements
text- the only field analyzed; up to 4,096 Unicode code points. Over the limit is rejected withinput_too_long, never truncated.language- must be a language this analyzer supports. Unsupported codes are rejected with400 unsupported_language, never analyzed as a different language.analyzers- include"fraud-scam"; up to 2 analyzers per request, one billed run each.- No context beyond
text- no user IDs, history, profiles, or attachments are read or inferred. - Analyzes the submitted text only - sender identity, headers, and account relationships are not inputs.
Ordinary positive
“Please update our payment details - our account is under audit, so wire this month's invoices to the new account below.”
Safe inference: The text matches documented payment-diversion patterns - hold the payment change and verify out-of-band before any wire.
Confusing negative · Quotation
“Security bulletin: watch for emails like "wire this month's invoices to the new account below" - report them to finance.”
What happens: Payment-diversion signals can fire on the quoted lure. Training material, bulletins, and forwarded suspect emails all contain the same language as the attack itself - the signal describes the text, not whether this message is itself an attack.
Why it's tricky: A bare request like "can you send me $200?" may also be too thin to match strongly - fraud patterns are contextual. Treat flagged text as a verify-first trigger, and unflagged payment changes as "not detected" rather than verified-safe.
What you can safely infer
- Which documented fraud patterns the text matched, and at what signal-match strength.
- That the same message on the same pinned configuration replays identically - the receipt supports your audit trail.
What you must not infer
- ✕That a flagged message proves fraud or wrongdoing - signals route verification, they do not determine guilt.
- ✕That the sender is a criminal - impersonation signals describe the language, not the person.
- ✕That an unflagged payment request is verified-safe - always confirm payment changes out-of-band regardless of the result.
Boundary: Fraud signals are not proof of wrongdoing. They flag language consistent with documented schemes so an analyst or control step can verify - payment changes should always be confirmed out-of-band regardless of the result.
Access the taxonomy through the API
Check Fraud & Scam Detection availability and endpoint programmatically via the public catalog endpoint - no authentication required. Include your API key to also retrieve taxonomy size and supported languages.
curl -X GET https://sentiqra.tech/v1/meta/domains/fraud-scam \
-H "Authorization: Bearer sq_live_your_key_here"Example response (authenticated):
{
"catalog_version": "...",
"domain": {
"id": "fraud_scam",
"name": "Fraud & Scam Detection",
"status": "production",
"taxonomy": { "groups": 4, "detection_labels": 200 },
"languages": [ ... ]
}
}Signals returned by the analyzers use code for the category or label slug - these correspond to the categories and indicators documented on this page.