post
https://api.prembly.com/api/v1/fraud/transaction-monitoring/screen-transaction
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Request Parameters
| Field | Type | Description |
|---|---|---|
| transaction_id | string | Unique transaction reference |
| customer_id | string | Unique customer identifier |
| amount | number | Transaction amount |
| currency | string | ISO currency code (e.g NGN) |
| transaction_type | string | transfer,withdrawal, deposit |
| channel | string | mobile, web, atm, branch |
| timestamp | string (ISO 8601) | Transaction date & time |
| source_account | string | Originating account |
| customer_email | string | Optional. Enables sanctions/PEP screening on the customer and digital footprint email intelligence (disposable-domain + MX deliverability checks) |
| beneficiary_name | string | Optional. beneficiary name |
| customer_name | string | Customer name |
| ip_address | string | ip address of the actor of the transaction |
What This Endpoint Screens
This endpoint screens both the sender and the receiver in a transaction, plus the transaction's country. The following checks run automatically on every call:
- Sanctions & PEP screening -- the customer and, where available, the counterparty are checked against global sanctions and PEP watchlists, plus a country-level sanctions check. A sanctions hit forces
blocked; a PEP hit forcesflaggedfor review. Returned undersanctions_screening(sender,receiver,country_check). - Crypto / stablecoin screening -- when a transaction involves a crypto or stablecoin transfer, wallet addresses are checked against known mixer/tumbler patterns. Returned under
crypto_screening(nullwhen not applicable). - Digital footprint / email intelligence -- when
customer_emailis provided, it's checked for disposable/temporary email domains and live MX record deliverability. Returned underdigital_footprint. Each organisation can configure whether -- and by how much -- a disposable or undeliverable email adds to the risk score (0 by default). - Risk score breakdown --
risk_score_breakdownshows exactly howrisk_scorewas calculated: the ML component, the rule component (sum of matched rule severity points), and anyscreening_boostapplied from sanctions/PEP/email-intelligence hits, so the math always reconciles with the final score. - Rules triggered --
rules_triggered_detailslists every rule that matched, with its name, severity, the exact condition that fired, and whether it was suppressed for that customer via a safelist entry.
