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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.
⚠️ 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.

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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?”

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Match Score vs Risk Score

AspectMatch ScoreRisk Score
QuestionIs this the same person?How risky is this entity?
PurposeClassify matches as False Positive vs Possible MatchDetermine final AML status
FactorsName, DOB, Country, Document NumberCountry, Category, Criminal Records
ThresholdMatch Score Threshold (default: 93)Approve Threshold / Review Threshold

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How Match Score Determines Review Status

Each match is classified based on its match score:
Match ScoreReview StatusMeaning
Score below Match Score ThresholdFalse PositiveMatch is likely NOT the same person
Score at or above Match Score ThresholdUnreviewedMatch 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)

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Review Statuses Explained

All AML matches start with one of two initial statuses based on their match score:
StatusDescriptionWhen Set
False PositiveThe match is likely NOT the same person as the screened individual. Excluded from risk assessment.Match score < threshold
UnreviewedThe 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:
StatusDescription
Confirmed MatchThe match has been verified as the same person.
InconclusiveUnable to determine if the match is the same person.

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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.

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How the Match Score is Calculated

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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)
ComponentDefault WeightDescription
Name60%Fuzzy string similarity between screened name and match name
Date of Birth25%Exact, partial (year only), or mismatch scoring
Country/Nationality15%Comparison between screened nationality and match countries
Note: Weights must always sum to 100%.

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Step 2: Document Number “Golden Key” Logic

After calculating the base score, the document number is evaluated separately using special override logic:
ScenarioEffectExample
MatchOverride score to 100Passport numbers match exactly
NeutralKeep base score unchangedDifferent document types, or one side missing
Hard MismatchApply 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.

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Component Scoring Details

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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

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Date of Birth Scoring

ScenarioScoreExample
Exact Match100%“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 Data0% (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.

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Country/Nationality Scoring

ScenarioScoreExample
Exact Match100%“ES” matches “Spain” or “ESP”
No Data on Match0% (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.

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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.

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Example: Name-Only Screening

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

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Example: Missing Country on Match

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

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Configuration Options

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

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Match Score Threshold

SettingDefaultDescription
Match Score Threshold93Matches below this are classified as False Positive. Matches at or above are Unreviewed (Possible Matches) that need review.

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Weights (must sum to 100)

SettingDefaultDescription
Name Weight60Weight for name similarity (0-100)
DOB Weight25Weight for date of birth (0-100)
Country Weight15Weight for country/nationality (0-100)

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API Request Example

{
  "full_name": "David Sánchez Pérez-Castejón",
  "date_of_birth": "1974-01-01",
  "nationality": "ES",
  "document_number": "ABC123456",
  "aml_name_weight": 60,
  "aml_dob_weight": 25,
  "aml_country_weight": 15,
  "aml_match_score_threshold": 93
}

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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
  }
}

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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                    │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

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Calculation Examples

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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:
  1. Name Score: 90% (fuzzy match)
  2. DOB Score: 100% (match only has year, year matches)
  3. 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

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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:
  1. Name Score: 72% (weak fuzzy match)
  2. DOB Score: -100% (year mismatch penalty)
  3. 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

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Example 3: Golden Key Override

Screened Data:
  • Name: “John D. Smith”
  • Document Number: “A12345678”
Match Data:
  • Name: “Jonathan David Smith”
  • Document Number: “A12345678”
Calculation:
  1. Base Score: ~70 (name fuzzy match)
  2. Document Number: MATCH (exact match)
Result: Score overridden to 100 (Golden Key match) → Unreviewed

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Managing Review Status

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

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In the Console

  1. Navigate to the session’s AML overview
  2. Click on a specific match to view details
  3. 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

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Best Practices

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