Didit Aml Screening

Data & APIs

Integrate Didit AML Screening standalone API to screen individuals or companies against global watchlists. Use when the user wants to perform AML checks, screen against sanctions lists, check PEP status, detect adverse media, implement KYC/AML compliance, screen against OFAC/UN/EU watchlists, calculate risk scores, or perform anti-money laundering screening using Didit. Supports 1300+ databases, fuzzy name matching, configurable scoring weights, and continuous monitoring.

Install

openclaw skills install didit-aml-screening

Didit AML Screening API

Overview

Screens individuals or companies against 1,300+ global watchlists and high-risk databases in real-time. Uses a two-score system: Match Score (identity confidence) and Risk Score (threat level).

Key constraints:

full_name is the only required field
Supports entity_type: "person" (default) or "company"
Document number acts as a "Golden Key" for definitive matching
All weight parameters must sum to 100

Coverage: OFAC SDN, UN, EU, HM Treasury, Interpol, FBI, 170+ national sanction lists, PEP Levels 1-4, 50,000+ adverse media sources, financial crime databases.

Scoring system:

Match Score (0-100): Is this the same person? → classifies hits as False Positive or Unreviewed
Risk Score (0-100): How risky is this entity? → determines final AML status

API Reference: https://docs.didit.me/standalone-apis/aml-screening Feature Guide: https://docs.didit.me/core-technology/aml-screening/overview Risk Scoring: https://docs.didit.me/core-technology/aml-screening/aml-risk-score

Authentication

All requests require x-api-key header. Get your key from Didit Business Console → API & Webhooks, or via programmatic registration (see below).

Getting Started (No Account Yet?)

If you don't have a Didit API key, create one in 2 API calls:

Register: POST https://apx.didit.me/auth/v2/programmatic/register/ with {"email": "you@gmail.com", "password": "MyStr0ng!Pass"}
Check email for a 6-character OTP code
Verify: POST https://apx.didit.me/auth/v2/programmatic/verify-email/ with {"email": "you@gmail.com", "code": "A3K9F2"} → response includes api_key

To add credits: GET /v3/billing/balance/ to check, POST /v3/billing/top-up/ with {"amount_in_dollars": 50} for a Stripe checkout link.

See the didit-verification-management skill for full platform management (workflows, sessions, users, billing).

Endpoint

POST https://verification.didit.me/v3/aml/

Headers

Header	Value	Required
`x-api-key`	Your API key	Yes
`Content-Type`	`application/json`	Yes

Body (JSON)

Parameter	Type	Required	Default	Description
`full_name`	string	Yes	—	Full name of person or entity
`date_of_birth`	string	No	—	DOB in `YYYY-MM-DD` format
`nationality`	string	No	—	ISO country code (alpha-2 or alpha-3)
`document_number`	string	No	—	ID document number ("Golden Key")
`entity_type`	string	No	`"person"`	`"person"` or `"company"`
`aml_name_weight`	integer	No	`60`	Name weight in match score (0-100)
`aml_dob_weight`	integer	No	`25`	DOB weight in match score (0-100)
`aml_country_weight`	integer	No	`15`	Country weight in match score (0-100)
`aml_match_score_threshold`	integer	No	`93`	Below = False Positive, at/above = Unreviewed
`save_api_request`	boolean	No	`true`	Save in Business Console
`vendor_data`	string	No	—	Your identifier for session tracking

Example

import requests

response = requests.post(
    "https://verification.didit.me/v3/aml/",
    headers={"x-api-key": "YOUR_API_KEY", "Content-Type": "application/json"},
    json={
        "full_name": "John Smith",
        "date_of_birth": "1985-03-15",
        "nationality": "US",
        "document_number": "AB1234567",
        "entity_type": "person",
    },
)
print(response.json())

const response = await fetch("https://verification.didit.me/v3/aml/", {
  method: "POST",
  headers: { "x-api-key": "YOUR_API_KEY", "Content-Type": "application/json" },
  body: JSON.stringify({
    full_name: "John Smith",
    date_of_birth: "1985-03-15",
    nationality: "US",
  }),
});

Response (200 OK)

{
  "request_id": "a1b2c3d4-...",
  "aml": {
    "status": "Approved",
    "total_hits": 2,
    "score": 45.5,
    "hits": [
      {
        "id": "hit-uuid",
        "caption": "John Smith",
        "match_score": 85,
        "risk_score": 45.5,
        "review_status": "False Positive",
        "datasets": ["PEP"],
        "properties": {"name": ["John Smith"], "country": ["US"]},
        "score_breakdown": {
          "name_score": 95, "name_weight": 60,
          "dob_score": 100, "dob_weight": 25,
          "country_score": 100, "country_weight": 15
        },
        "risk_view": {
          "categories": {"score": 55, "risk_level": "High"},
          "countries": {"score": 23, "risk_level": "Low"},
          "crimes": {"score": 0, "risk_level": "Low"}
        }
      }
    ],
    "screened_data": {
      "full_name": "John Smith",
      "date_of_birth": "1985-03-15",
      "nationality": "US",
      "document_number": "AB1234567"
    },
    "warnings": []
  }
}

Match Score System

Formula: (Name × W1) + (DOB × W2) + (Country × W3)

Component	Default Weight	Algorithm
Name	60%	RapidFuzz WRatio — handles typos, word order, middle name variations
DOB	25%	Exact=100%, Year-only=100%, Same year diff date=50%, Mismatch=-100%
Country	15%	Exact=100%, Mismatch=-50%, Missing=0%. Auto-converts ISO codes

Document Number "Golden Key":

Scenario	Effect
Same type, same value	Override score to 100
Different type or one missing	Keep base score (neutral)
Same type, different value	-50 point penalty

Classification: Score < threshold (default 93) → False Positive. Score >= threshold → Unreviewed.

When data is missing, remaining weights are re-normalized. E.g., name-only → name weight becomes 100%.

Risk Score System

Formula: (Country × 0.30) + (Category × 0.50) + (Criminal × 0.20)

Final AML Status (from highest risk score among non-FP hits):

Highest Risk Score	Status
Below 80 (default)	Approved
Between 80-100	In Review
Above 100	Declined
All False Positives	Approved

Category scores (50% weight):

Category	Score
Sanctions / PEP Level 1	100
Warnings & Regulatory	95
PEP Level 2 / Insolvency	80
Adverse Media	60
PEP Level 4 / Businessperson	55

Status Values & Handling

Status	Meaning	Action
`"Approved"`	No significant matches or all False Positives	Safe to proceed
`"In Review"`	Matches found with moderate risk	Manual compliance review needed
`"Rejected"`	High-risk matches confirmed	Block or escalate per your policy
`"Not Started"`	Screening not yet performed	Check for missing data

Error Responses

Code	Meaning	Action
`400`	Invalid request body	Check `full_name` and parameter formats
`401`	Invalid API key	Verify `x-api-key` header
`403`	Insufficient credits	Check credits in Business Console

Warning Tags

Tag	Description
`POSSIBLE_MATCH_FOUND`	Potential watchlist matches requiring review
`COULD_NOT_PERFORM_AML_SCREENING`	Missing KYC data. Provide full name, DOB, nationality, document number

Response Field Reference

Hit Object

Field	Type	Description
`match_score`	integer	0-100 identity confidence score
`risk_score`	float	0-100 threat level score
`review_status`	string	`"False Positive"`, `"Unreviewed"`, `"Confirmed Match"`, `"Inconclusive"`
`datasets`	array	e.g. `["Sanctions"]`, `["PEP"]`, `["Adverse Media"]`
`pep_matches`	array	PEP match details
`sanction_matches`	array	Sanction match details
`adverse_media_matches`	array	`{headline, summary, source_url, sentiment_score, adverse_keywords}`
`linked_entities`	array	Related persons/entities
`first_seen` / `last_seen`	string	ISO 8601 timestamps

Adverse media sentiment: -1 = slightly negative, -2 = moderately, -3 = highly negative.

Continuous Monitoring

Available on Pro plan. Automatically included for all AML-screened sessions.

Daily automated re-screening against updated watchlists
New hits → session status updated to "In Review" or "Declined" based on thresholds
Real-time webhook notifications on status changes
Zero additional integration — uses same thresholds from workflow config

Common Workflows

Basic AML Check

1. POST /v3/aml/ → {"full_name": "John Smith", "nationality": "US"}
2. If "Approved" → no significant watchlist matches
   If "In Review" → review hits[].datasets, hits[].risk_view for details
   If "Rejected" → block user, check hits for sanctions/PEP details

Comprehensive KYC + AML

1. POST /v3/id-verification/ → extract name, DOB, nationality, document number
2. POST /v3/aml/ → screen extracted data with all fields populated
3. More data = higher match accuracy = fewer false positives

Utility Scripts

screen_aml.py: Screen against AML watchlists from the command line.

# Requires: pip install requests
export DIDIT_API_KEY="your_api_key"
python scripts/screen_aml.py --name "John Smith"
python scripts/screen_aml.py --name "John Smith" --dob 1985-03-15 --nationality US
python scripts/screen_aml.py --name "Acme Corp" --entity-type company