didit-aml-screening

name	didit-aml-screening
description	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.
version	1.0.0
metadata	{"openclaw":{"requires":{"env":"[Truncated]"},"primaryEnv":"DIDIT_API_KEY","emoji":"🛡️","homepage":"https://docs.didit.me"}}

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": "[e-mail]", "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": "[e-mail]", "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"]  
         
             
             
             
        
         
              
              
              
        
      
    
     
       
       
       
       
    
     
  

---

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