AML Match Score

The Match Score is a weighted confidence metric that determines how closely a potential AML match corresponds to the screened individual. This score is used to classify individual matches as either False Positives or Possible Matches that require further review.

Didit AML match score configuration showing weighted name, DOB and country thresholds

⚠️ Important: The Match Score determines individual match classification, NOT the final AML status. The final AML status (Approved/In Review/Declined) is determined by the Risk Score of non-false-positive matches.

Overview

When screening a person against AML watchlists, each potential match receives a match score from 0-100. This score answers the question: “Is this match actually the same person we’re screening?”

Match Score vs Risk Score

Aspect	Match Score	Risk Score
Question	Is this the same person?	How risky is this entity?
Purpose	Classify matches as False Positive vs Possible Match	Determine final AML status
Factors	Name, DOB, Country, Document Number	Country, Category, Criminal Records
Threshold	Match Score Threshold (default: 93)	Approve Threshold / Review Threshold

How Match Score Determines Review Status

Each match is classified based on its match score:

Match Score	Review Status	Meaning
Score below Match Score Threshold	False Positive	Match is likely NOT the same person
Score at or above Match Score Threshold	Unreviewed	Match requires manual review

Default threshold: 93%

Example: With a threshold of 93:

Match with score 85 → False Positive (auto-dismissed)
Match with score 95 → Unreviewed (needs review, risk score determines urgency)

Review Statuses Explained

All AML matches start with one of two initial statuses based on their match score:

Status	Description	When Set
False Positive	The match is likely NOT the same person as the screened individual. Excluded from risk assessment.	Match score < threshold
Unreviewed	The match is a possible match that requires manual review to confirm or dismiss.	Match score ≥ threshold

After manual review, compliance officers can update the status to:

Status	Description
Confirmed Match	The match has been verified as the same person.
Inconclusive	Unable to determine if the match is the same person.

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

How the Match Score is Calculated

Step 1: Base Score Calculation

The base score is calculated using three components with configurable weights:

Base Score = (Name Score × Name Weight) + (DOB Score × DOB Weight) + (Country Score × Country Weight)

Component	Default Weight	Description
Name	60%	Fuzzy string similarity between screened name and match name
Date of Birth	25%	Exact, partial (year only), or mismatch scoring
Country/Nationality	15%	Comparison between screened nationality and match countries

Note: Weights must always sum to 100%.

Step 2: Document Number “Golden Key” Logic

After calculating the base score, the document number is evaluated separately using special override logic:

Scenario	Effect	Example
Match	Override score to 100	Passport numbers match exactly
Neutral	Keep base score unchanged	Different document types, or one side missing
Hard Mismatch	Apply penalty (-50 points)	Same document type but different values

This approach recognizes that a matching document number is definitive proof of identity (the “Golden Key”), while mismatched document types shouldn’t penalize the score.

Component Scoring Details

Name Similarity (0-100)

Name matching uses the WRatio algorithm from RapidFuzz, which is robust to:

Typos and misspellings
Word order differences (“John Smith” vs “Smith, John”)
Middle name variations (“Robert J. Smith” vs “Robert James Smith”)
Length differences

Date of Birth Scoring

Scenario	Score	Example
Exact Match	100%	“1985-03-15” matches “1985-03-15”
Year Match (match has year only)	100%	“1985-03-15” matches “1985” — full match because match only provides year-level precision
Year Match (different day/month)	50%	“1985-03-15” matches “1985-06-20” — same year but dates differ
Year Mismatch	-100% (penalty)	“1985-03-15” does not match “1990-03-15”
No Data	0% (neutral)	Either side missing DOB

Important: When the match only provides a year (e.g., “1974”), matching that year counts as a full match because that’s all the information available to verify.

Country/Nationality Scoring

Scenario	Score	Example
Exact Match	100%	“ES” matches “Spain” or “ESP”
No Data on Match	0% (neutral)	Match has no country information
Mismatch	-50% (penalty)	“ES” does not match “France”

The system automatically converts between:

ISO alpha-2 codes (ES)
ISO alpha-3 codes (ESP)
Full country names (Spain)

It also checks the citizenship field in addition to countries.

Score Normalization (Re-weighting)

When data is missing from either the screened person or the match, the system uses score normalization to avoid penalizing for unavailable information.

Example: Name-Only Screening

If you screen with only a name (no DOB or country):

Original Weights	Normalized Weights
Name: 60%	Name: 100%
DOB: 25%	DOB: 0% (not comparable)
Country: 15%	Country: 0% (not comparable)

Result: The name score becomes the entire match score.

Example: Missing Country on Match

If the match doesn’t have country data but has DOB:

Original Weights	Normalized Weights
Name: 60%	Name: 70.6%
DOB: 25%	DOB: 29.4%
Country: 15%	Country: 0% (not comparable)

This ensures fair scoring regardless of data availability.

Configuration Options

You can customize the match score calculation via the API or workflow settings:

Match Score Threshold

Setting	Default	Description
Match Score Threshold	93	Matches below this are classified as False Positive. Matches at or above are Unreviewed (Possible Matches) that need review.

Weights (must sum to 100)

Setting	Default	Description
Name Weight	60	Weight for name similarity (0-100)
DOB Weight	25	Weight for date of birth (0-100)
Country Weight	15	Weight for country/nationality (0-100)

API Request Example

{
  "full_name": "John Doe",
  "date_of_birth": "1990-01-01",
  "nationality": "USA",
  "document_number": "SAMPLE-DOC-12345",
  "aml_name_weight": 60,
  "aml_dob_weight": 25,
  "aml_country_weight": 15,
  "aml_match_score_threshold": 93
}

Response: Score Breakdown

Each match in the response includes:

match_score — The calculated match score (0-100)
risk_score — The calculated risk score (0-100) - see Risk Score
review_status — “False Positive” or “Unreviewed” based on match score threshold
score_breakdown — Detailed breakdown of the match score calculation

{
  "caption": "David Sanchez",
  "match_score": 95,
  "risk_score": 65.5,
  "review_status": "Unreviewed",
  "score_breakdown": {
    "name_score": 95,
    "name_weight": 60,
    "name_weight_normalized": 70.59,
    "name_contribution": 67.06,
    "dob_score": 100,
    "dob_weight": 25,
    "dob_weight_normalized": 29.41,
    "dob_contribution": 29.41,
    "country_score": 0,
    "country_weight": 15,
    "country_weight_normalized": 0,
    "country_contribution": 0,
    "document_number_match_type": "NEUTRAL",
    "document_number_effect": "No document number provided for screening",
    "total_score": 95
  }
}

Complete Flow: From Match Score to Final AML Status

┌─────────────────────────────────────────────────────────────────────────────┐
│                           AML SCREENING FLOW                                 │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  1. RETRIEVE MATCHES FROM WATCHLISTS                                        │
│     └── All matches with score ≥ 75% are included                           │
│                                                                             │
│  2. CALCULATE MATCH SCORE FOR EACH MATCH                                    │
│     Match Score = (Name × W1) + (DOB × W2) + (Country × W3)                │
│     + Golden Key document number logic                                      │
│                                                                             │
│  3. CLASSIFY MATCHES BY MATCH SCORE (Match Score Threshold = 93)           │
│     ├── Match Score < 93 → FALSE POSITIVE (excluded from risk assessment)  │
│     └── Match Score ≥ 93 → UNREVIEWED (included in risk assessment)        │
│                                                                             │
│  4. CALCULATE RISK SCORE FOR NON-FALSE-POSITIVE MATCHES                     │
│     Risk Score = (Country × 30%) + (Category × 50%) + (Criminal × 20%)     │
│                                                                             │
│  5. DETERMINE FINAL AML STATUS (based on highest risk score)               │
│     ├── Highest Risk Score < approve_threshold → APPROVED                   │
│     ├── Highest Risk Score ≤ review_threshold → IN REVIEW                   │
│     └── Highest Risk Score > review_threshold → DECLINED                    │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

Calculation Examples

Example 1: Strong Match → Unreviewed

Screened Data:

Name: “Robert J. Smith”
DOB: “1985-03-15”
Country: “US”

Match Data:

Name: “Robert James Smith”
DOB: “1985”
Country: “United States”

Calculation:

Name Score: 90% (fuzzy match)
DOB Score: 100% (match only has year, year matches)
Country Score: 100% (exact match)

Match Score = (90 × 0.60) + (100 × 0.25) + (100 × 0.15)
            = 54 + 25 + 15
            = 94

Result: Match Score (94) ≥ threshold (93) → Unreviewed → Risk score will be calculated and used for final AML status

Example 2: Weak Match → False Positive

Screened Data:

Name: “John Smith”
DOB: “1990-05-20”
Country: “US”

Match Data:

Name: “Johnny Smithson”
DOB: “1975”
Country: “Canada”

Calculation:

Name Score: 72% (weak fuzzy match)
DOB Score: -100% (year mismatch penalty)
Country Score: -50% (country mismatch penalty)

Match Score = (72 × 0.60) + (-100 × 0.25) + (-50 × 0.15)
            = 43.2 - 25 - 7.5
            = 10.7 → 11

Result: Match Score (11) < threshold (93) → False Positive → This match is excluded from risk assessment

Example 3: Golden Key Override

Screened Data:

Name: “John D. Smith”
Document Number: “A12345678”

Match Data:

Name: “Jonathan David Smith”
Document Number: “A12345678”

Calculation:

Base Score: ~70 (name fuzzy match)
Document Number: MATCH (exact match)

Result: Score overridden to 100 (Golden Key match) → Unreviewed

Managing Review Status

After the initial classification, you can manually update each match’s review status:

In the Console

Navigate to the session’s AML overview
Click on a specific match to view details
Use the status dropdown to change the review status:
- Confirmed Match — Verify the match is the same person
- False Positive — Mark the match as not matching
- Inconclusive — Unable to determine

Best Practices

Start with default threshold (93) — This provides a good balance between catching true matches and filtering false positives.
Lower threshold for high-risk scenarios — If you need to be more cautious, lower the Match Score Threshold to catch more potential matches.
Use document numbers when available — They provide the strongest identity confirmation and can override low match scores.
Review normalized weights — Check the normalized weight fields in the score breakdown to understand how missing data affected the score.
Monitor false positive rates — If too many legitimate matches are being marked as false positives, lower the threshold.

Authentication

Verification

Monitoring

Platform

Overview

Match Score vs Risk Score

How Match Score Determines Review Status

Review Statuses Explained

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

How the Match Score is Calculated

Step 1: Base Score Calculation

Step 2: Document Number “Golden Key” Logic

Component Scoring Details

Name Similarity (0-100)

Date of Birth Scoring

Country/Nationality Scoring

Score Normalization (Re-weighting)

Example: Name-Only Screening

Example: Missing Country on Match

Configuration Options

Match Score Threshold

Weights (must sum to 100)

API Request Example

Response: Score Breakdown

Complete Flow: From Match Score to Final AML Status

Calculation Examples

Example 1: Strong Match → Unreviewed

Example 2: Weak Match → False Positive

Example 3: Golden Key Override

Managing Review Status

In the Console

Best Practices

​Overview

​Match Score vs Risk Score

​How Match Score Determines Review Status

​Review Statuses Explained

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

​How the Match Score is Calculated

​Step 1: Base Score Calculation

​Step 2: Document Number “Golden Key” Logic

​Component Scoring Details

​Name Similarity (0-100)

​Date of Birth Scoring

​Country/Nationality Scoring

​Score Normalization (Re-weighting)

​Example: Name-Only Screening

​Example: Missing Country on Match

​Configuration Options

​Match Score Threshold

​Weights (must sum to 100)

​API Request Example

​Response: Score Breakdown

​Complete Flow: From Match Score to Final AML Status

​Calculation Examples

​Example 1: Strong Match → Unreviewed

​Example 2: Weak Match → False Positive

​Example 3: Golden Key Override

​Managing Review Status

​In the Console

​Best Practices

Overview

Match Score vs Risk Score

How Match Score Determines Review Status

Review Statuses Explained

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

How the Match Score is Calculated

Step 1: Base Score Calculation

Step 2: Document Number “Golden Key” Logic

Component Scoring Details

Name Similarity (0-100)

Date of Birth Scoring

Country/Nationality Scoring

Score Normalization (Re-weighting)

Example: Name-Only Screening

Example: Missing Country on Match

Configuration Options

Match Score Threshold

Weights (must sum to 100)

API Request Example

Response: Score Breakdown

Complete Flow: From Match Score to Final AML Status

Calculation Examples

Example 1: Strong Match → Unreviewed

Example 2: Weak Match → False Positive

Example 3: Golden Key Override

Managing Review Status

In the Console

Best Practices