AML Match Score

Documentation Index

Fetch the complete documentation index at: https://docs.didit.me/llms.txt

Use this file to discover all available pages before exploring further.

⚠️ 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

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Match Score vs Risk Score

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How Match Score Determines Review Status

Default threshold: 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

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How the Match Score is Calculated

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Step 1: Base Score Calculation

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

Note: Weights must always sum to 100%.

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Step 2: Document Number “Golden Key” Logic

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Component Scoring Details

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Name Similarity (0-100)

• 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

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

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

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Score Normalization (Re-weighting)

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Example: Name-Only Screening

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Example: Missing Country on Match

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Configuration Options

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Match Score Threshold

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Weights (must sum to 100)

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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
}

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Response: Score Breakdown

• 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

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

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

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

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Example 2: Weak Match → False Positive

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

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

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

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Example 3: Golden Key Override

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

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

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

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Managing 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.