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:

1. Match Score (0-100): Is this the same person? → classifies hits as False Positive or Unreviewed
2. 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:

1. Register: POST https://apx.didit.me/auth/v2/programmatic/register/ with {"email": "you@gmail.com", "password": "MyStr0ng!Pass"}
2. Check email for a 6-character OTP code
3. 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

Body (JSON)

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)

Document Number "Golden Key":

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):

Category scores (50% weight):

Status Values & Handling

Error Responses

Warning Tags

Response Field Reference

Hit Object

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