AML Screening Overview

Didit’s AML Screening provides real-time risk detection by screening users against global watchlists and databases. The solution combines advanced data matching with AI-powered risk assessment to ensure regulatory compliance while maintaining a smooth user experience. The Academy lesson on reading a verification result reaches AML screening at 8:40: sanctions, PEPs, and adverse media on a real session.

Didit AML screening watchlist sources table including OFAC, EU, UN sanctions and PEP lists

Key Concepts: Match Score vs Risk Score

Didit uses a two-score system for AML screening:

Match Score (Identity Confidence)

Question: Is this match the same person we’re screening?
Factors: Name similarity, Date of Birth, Country/Nationality, Document Number
Purpose: Classifies match as False Positive or Unreviewed (Possible Match)
Threshold: Match Score Threshold (default: 93)

Risk Score (Entity Risk Level)

Question: How risky is this entity if it’s a true match?
Factors: Country risk, Category (PEP/Sanctions/etc.), Criminal records
Purpose: Determines the final AML status (Approved/In Review/Declined)
Thresholds: Approve Threshold (default: 80) and Review Threshold (default: 100)

┌─────────────────────────────────────────────────────────────────────────────┐
│                           AML SCREENING FLOW                                │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  STEP 1: Screen against watchlists                                          │
│     └── Returns potential matches (match score ≥ 75% included)              │
│                                                                             │
│  STEP 2: For each match, calculate MATCH SCORE                              │
│     └── Match Score = (Name × W1) + (DOB × W2) + (Country × W3)             │
│                                                                             │
│  STEP 3: Classify match by match score                                      │
│     ├── Match Score < 93 → FALSE POSITIVE (excluded from risk)              │
│     └── Match Score ≥ 93 → UNREVIEWED (included in risk)                    │
│                                                                             │
│  STEP 4: For unreviewed matches, calculate RISK SCORE                       │
│     └── Risk Score = (Country × 30%) + (Category × 50%) + (Criminal × 20%)  │
│                                                                             │
│  STEP 5: Determine AML STATUS (highest risk score among non-FP matches)     │
│     ├── Risk Score < 86 → APPROVED                                          │
│     ├── 86 ≤ Risk Score ≤ 100 → IN REVIEW                                   │
│     └── Risk Score > 100 → DECLINED                                         │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

Match Review Statuses

Each AML match is assigned a review status based on its match score:

Status	Description	When Assigned
False Positive	Match is likely NOT the same person	Match score < threshold (auto-assigned)
Unreviewed	Match is a possible match requiring review	Match score ≥ threshold (auto-assigned)
Confirmed Match	Match has been verified as the same person	Manually set by compliance officer
Inconclusive	Unable to determine if match is the same person	Manually set by compliance officer

Tip: You can change a match review status in the Console by viewing the AML overview or clicking on a specific match to see its details.

Key Features

1. Comprehensive Data Extraction

Accurate Extraction: Extract data from user-provided information or identity documents using advanced OCR technology.
Fuzzy Logic: Account for name variations and misspellings to ensure comprehensive data capture.

2. Extensive Watchlist Coverage

Screen against multiple categories including:

Sanctions Lists: From government and international bodies.
Politically Exposed Persons (PEPs): Individuals with prominent public functions.
Criminal Records: Including global and local criminal databases.
Adverse Media Mentions: News articles and reports on financial crimes and other risks.
Custom Watchlists: Configurable based on specific client requirements.

3. Advanced Matching Algorithms

Fuzzy Matching: Catch slight variations in names or details to ensure accurate matching.
Multiple Data Points: Utilize name, date of birth, nationality, and other identifiers for thorough screening.
Golden Key Logic: Document number matching that can override scores for definitive identification.

4. Two-Score Risk Assessment

Each match receives TWO scores:

Score	Purpose	Classification
Match Score (0-100)	Is this the same person?	False Positive vs Unreviewed
Risk Score (0-100)	How risky is this entity?	Determines final AML status

Match Review Status (based on Match Score):

False Positive: Match score below threshold - likely NOT the same person
Unreviewed: Match score at or above threshold - requires review

Final AML Status (based on highest Risk Score among non-false-positive matches):

Approved: All unreviewed matches have low risk scores
In Review: At least one unreviewed match has medium-high risk score
Declined: At least one unreviewed match has very high risk score

5. Detailed Adverse Media Screening

Screen against a wide range of adverse media categories, including:

Financial Crimes: Money laundering, embezzlement, and more.
Violent Crimes: Assault, murder, and related offenses.
Terrorism: Involvement in or support for terrorist activities.
Narcotics: Drug trafficking and related crimes.
Fraud: Various types of fraud and deceptive practices.
Regulatory Violations: Breaches of regulatory compliance requirements.

6. Customizable Screening Parameters

Adjustable Sensitivity: Configure the screening sensitivity based on your organization’s risk appetite.
Customizable Watchlists: Select which watchlists and categories to include in the screening process to meet specific requirements.

Configurable Thresholds

You can customize security levels by setting different thresholds: Match Score Configuration:

Setting	Default	Description
Match Score Threshold	93	Matches below this are False Positives, at or above are Unreviewed
Name Weight	60	Weight for name in match score
DOB Weight	25	Weight for DOB in match score
Country Weight	15	Weight for country in match score

Risk Score Thresholds (Final Status):

Setting	Default	Description
Approve Threshold	80	Risk scores below = Approved
Review Threshold	100	Risk scores above = Declined; between = In Review

Didit AML risk score thresholds for Approved, In Review and Declined statuses

These thresholds can be adjusted based on your risk tolerance and security requirements.

How It Works

Our AML Screening process efficiently identifies potential risks while minimizing delays for legitimate users.

Data Extraction

The system extracts relevant information from either user-provided data or uploaded identity documents.

Input	Method
Identity documents	Advanced OCR technology for accurate data extraction
User-provided data	Direct input (name, DOB, nationality, document number)
Fuzzy logic	Accounts for name variations and misspellings

Watchlist Screening

The extracted data is cross-checked against 1,300+ global watchlists and databases in real time:

Sanctions Lists — Government and international bodies (OFAC, EU, UN, etc.)
Politically Exposed Persons (PEPs) — Individuals with prominent public functions
Criminal Records — Global and local criminal databases
Adverse Media — News articles and reports on financial crimes
Custom Watchlists — Configurable based on your requirements

Match Score Calculation

For each potential match, the system calculates an identity confidence score — how likely it is the same person.

Factor	Default Weight	Description
Name similarity	60%	Fuzzy matching accounting for variations
Date of Birth	25%	Exact or partial DOB comparison
Country/Nationality	15%	Country of origin comparison

Match Score below threshold (default: 93) → classified as False Positive
Match Score at or above threshold → classified as Unreviewed (possible match)
Golden Key: Document number match can override to 100% for definitive identification

Risk Score Calculation

For non-false-positive matches, the system calculates the entity’s risk level:

Factor	Weight	Description
Category	50%	PEP level, sanctions type, criminal severity
Country risk	30%	Risk rating of associated countries
Criminal records	20%	Presence and severity of criminal history

Final Status Determination

Based on the highest risk score among all non-false-positive matches:

Risk Score	Status	Action
Below approve threshold	Approved	Auto-cleared, no credible risk
Between thresholds	In Review	Flagged for manual compliance review
Above review threshold	Declined	High risk, automatic rejection

If all matches are false positives, the result is Approved (no credible matches found).

Result Generation

A detailed report is generated with full transparency:

Each match’s match score and risk score with breakdowns
Match classification (False Positive vs. Unreviewed vs. Confirmed)
Source database and category details for every hit
Results delivered via API response, webhook, or Business Console
Automatic decisioning based on your configured risk score thresholds

AML Hit Data Reference

Each AML hit returned in the API response contains detailed information about the matched entity. Below are the key fields and their possible values.

Dataset Categories

The datasets field on each hit indicates which compliance database categories the entity was found in. Possible values:

Value	Description
`PEP`	Politically Exposed Person (general)
`PEP Level 1`	Heads of state, senior politicians, supreme court judges
`PEP Level 2`	Members of parliament, ambassadors, military leaders
`PEP Level 3`	Local government officials, mid-level civil servants
`PEP Level 4`	RCA (Relatives and Close Associates) of PEP individuals
`Sanctions`	International and national sanctions lists (OFAC, EU, UN, etc.)
`Adverse Media`	Negative news articles and reports related to financial crime
`Warnings and Regulatory Enforcement`	Regulatory warnings, enforcement actions, and compliance alerts
`Fitness and Probity`	Fitness and probity registers from financial regulators
`SIP`	Special Interest Person lists
`SIE`	Special Interest Entity lists
`Insolvency`	Insolvency and bankruptcy registers

Sanction Matches

The sanction_matches array contains details for each sanction list where the entity was found. Key fields:

Field	Type	Description
`list_name`	`string[]`	Name(s) of the sanction list(s)
`matched_name`	`string`	Name as it appears in the sanction list
`description`	`string`	Description of the sanction entry
`reason`	`string`	Free-text reason why the entity was sanctioned. This is not an enum — values come directly from the sanctioning authority and describe the specific grounds for the listing (e.g., “Executive Order 13224 (Terrorism)”, “Support for actions undermining Ukraine’s sovereignty”).
`legal_basis`	`string[]`	Legal basis for the sanction
`sanction_program`	`string[]`	Sanction program(s) the entity is listed under
`sanctioning_authority`	`string[]`	Authority that imposed the sanction
`listed_on`	`string[]`	Date(s) when the entity was listed
`source_url`	`string`	URL to the source

Warning Matches

The warning_matches array contains details for matches from warning, regulatory enforcement, and related lists. The additional_data field may include a data.case_details array with structured case information.

Warning Case Types

The case_type field within additional_data.data.case_details[] describes the nature of the warning case. Known values:

Value	Description
`Accusation`	Formal accusation or charge
`Arrest`	Arrest record
`Bribery`	Bribery-related case
`Criminal`	General criminal case
`Fraud`	Fraud-related case
`Illegal Fundraising`	Illegal fundraising activity
`Non-conviction Terror`	Terrorism-related listing without conviction
`Proclaimed Offender`	Officially proclaimed offender
`Regulatory Enforcement`	Regulatory enforcement action
`Terrorism`	Terrorism-related case
`Terrorism-related`	Related to terrorism activities
`Terrorism-related listing`	Listed in connection with terrorism

Authentication

Verification

Monitoring

Platform

Key Concepts: Match Score vs Risk Score

Match Score (Identity Confidence)

Risk Score (Entity Risk Level)

Match Review Statuses

Tip: You can change a match review status in the Console by viewing the AML overview or clicking on a specific match to see its details.

Key Features

1. Comprehensive Data Extraction

2. Extensive Watchlist Coverage

3. Advanced Matching Algorithms

4. Two-Score Risk Assessment

5. Detailed Adverse Media Screening

6. Customizable Screening Parameters

Configurable Thresholds

How It Works

AML Hit Data Reference

Dataset Categories

Sanction Matches

Warning Matches

Warning Case Types

​Key Concepts: Match Score vs Risk Score

​Match Score (Identity Confidence)

​Risk Score (Entity Risk Level)

​Match Review Statuses

​Tip: You can change a match review status in the Console by viewing the AML overview or clicking on a specific match to see its details.

​Key Features

​1. Comprehensive Data Extraction

​2. Extensive Watchlist Coverage

​3. Advanced Matching Algorithms

​4. Two-Score Risk Assessment

​5. Detailed Adverse Media Screening

​6. Customizable Screening Parameters

​Configurable Thresholds

​How It Works

​AML Hit Data Reference

​Dataset Categories

​Sanction Matches

​Warning Matches

​Warning Case Types

Key Concepts: Match Score vs Risk Score

Match Score (Identity Confidence)

Risk Score (Entity Risk Level)

Match Review Statuses

Tip: You can change a match review status in the Console by viewing the AML overview or clicking on a specific match to see its details.

Key Features

1. Comprehensive Data Extraction

2. Extensive Watchlist Coverage

3. Advanced Matching Algorithms

4. Two-Score Risk Assessment

5. Detailed Adverse Media Screening

6. Customizable Screening Parameters

Configurable Thresholds

How It Works

AML Hit Data Reference

Dataset Categories

Sanction Matches

Warning Matches

Warning Case Types