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The Risk Score is a quantitative assessment that evaluates how risky an AML hit entity is. This score is used to determine the final AML status (Approved/In Review/Declined) based on the highest risk score among all non-false-positive hits.
⚠️ Important: The Risk Score determines the final AML status, NOT individual hit classification. Individual hits are first classified as False Positive vs Unreviewed using the Match Score.

Overview

Each AML hit receives a risk score from 0-100, calculated by combining three key risk factors. This score answers the question: “If this is a true match, how risky is this entity?”

Risk Score vs Match Score

AspectMatch ScoreRisk Score
QuestionIs this the same person?How risky is this entity?
PurposeClassify hits as False Positive vs UnreviewedDetermine final AML status
FactorsName, DOB, Country, Document NumberCountry, Category, Criminal Records
Applied toAll hitsOnly non-false-positive hits

How Risk Score Determines Final AML Status

The final AML status is determined by the highest risk score among all hits that are NOT classified as False Positive:
Highest Risk ScoreFinal AML Status
Score below Approve ThresholdApproved
Score between Approve and Review ThresholdsIn Review
Score above Review ThresholdDeclined
Default thresholds:
  • Approve Threshold: 80
  • Review Threshold: 100
Example: With default thresholds:
  • Highest risk score = 50 → Approved (low risk)
  • Highest risk score = 90 → In Review (medium risk, needs manual review)
  • Highest risk score = 101 (impossible, but >100) → Declined (high risk)

Risk Score Formula

The overall risk score is calculated using a weighted average:
Risk Score = (Country Score × 0.30) + (Category Score × 0.50) + (Criminal Score × 0.20)
ComponentWeightDescription
Country30%Geographic risk assessment based on AML/CFT factors
Category50%Risk level based on the type of watchlist listing
Criminal Records20%Risk from criminal history and convictions

Risk Levels

Based on the calculated risk score, entities are classified into three risk tiers:
Risk LevelScore RangeDescription
🟢 Low Risk< 30Minimal compliance concern, standard due diligence
🟡 Medium Risk30 – 49Elevated concern, enhanced due diligence recommended
🔴 High Risk≥ 50Significant concern, thorough investigation required

Component Scoring Details

Country Score (30% Weight)

The country score reflects the inherent AML/CFT risk associated with a jurisdiction. Factors include:
  • Money laundering and terrorist financing risks
  • Compliance with FATF recommendations
  • Presence of international sanctions
  • Corruption perception indices
  • Regulatory framework strength
Scoring Range: 0-100 (higher = more risk)
Note: When a hit is associated with multiple countries, the highest country score is used in the calculation.

Country Risk Scores

CountryCodeRisk ScoreRisk Level
🇮🇷 IranIR81.66🔴 High
🇰🇵 North KoreaKP78.20🔴 High
🇲🇲 MyanmarMM75.09🔴 High
🇦🇫 AfghanistanAF74.63🔴 High
🇸🇾 SyriaSY74.49🔴 High
🇭🇹 HaitiHT74.30🔴 High
🇨🇩 Democratic Republic of the CongoCD71.66🔴 High
🇷🇺 RussiaRU71.25🔴 High
🇻🇪 VenezuelaVE71.09🔴 High
🇸🇸 South SudanSS70.77🔴 High
🇾🇪 YemenYE68.40🔴 High
🇱🇧 LebanonLB67.76🔴 High
🇸🇴 SomaliaSO65.38🔴 High
🇲🇱 MaliML62.78🔴 High
🇱🇾 LibyaLY61.96🔴 High
🇲🇿 MozambiqueMZ57.15🔴 High
🇧🇮 BurundiBI56.48🔴 High
🇵🇭 PhilippinesPH56.44🔴 High
🇬🇼 Guinea-BissauGW55.99🔴 High
🇰🇪 KenyaKE55.49🔴 High
🇽🇰 KosovoXK55.49🔴 High
🇪🇷 EritreaER54.72🔴 High
🇵🇸 State of PalestinePS54.41🔴 High
🇳🇬 NigeriaNG54.40🔴 High
🇻🇳 VietnamVN54.24🔴 High
🇮🇶 IraqIQ54.15🔴 High
🇹🇿 TanzaniaTZ53.22🔴 High
🇭🇷 CroatiaHR53.03🔴 High
🇩🇿 AlgeriaDZ52.28🔴 High
🇵🇦 PanamaPA52.28🔴 High
🇨🇮 Cote D’IvoireCI51.61🔴 High
🇨🇲 CameroonCM51.35🔴 High
🇨🇫 Central African RepublicCF50.98🔴 High
🇸🇩 SudanSD49.52🔴 High
🇦🇴 AngolaAO49.47🟡 Medium
🇳🇮 NicaraguaNI49.07🟡 Medium
🇹🇹 Trinidad and TobagoTT48.45🟡 Medium
🇧🇫 Burkina FasoBF47.94🟡 Medium
🇹🇷 TurkeyTR47.93🟡 Medium
🇻🇺 VanuatuVU47.93🟡 Medium
🇳🇪 NigerNE47.02🟡 Medium
🇬🇹 GuatemalaGT47.01🟡 Medium
🇧🇦 Bosnia and HerzegovinaBA46.75🟡 Medium
🇺🇬 UgandaUG46.67🟡 Medium
🇿🇦 South AfricaZA46.60🟡 Medium
🇺🇦 UkraineUA46.43🟡 Medium
🇨🇳 ChinaCN45.92🟡 Medium
🇨🇺 CubaCU45.89🟡 Medium
🇧🇬 BulgariaBG45.70🟡 Medium
🇦🇪 United Arab EmiratesAE45.39🟡 Medium
🇿🇼 ZimbabweZW45.37🟡 Medium
🇦🇱 AlbaniaAL45.34🟡 Medium
🇯🇲 JamaicaJM45.11🟡 Medium
🇧🇧 BarbadosBB45.04🟡 Medium
🇬🇳 GuineaGN44.80🟡 Medium
🇸🇽 Sint MaartenSX44.79🟡 Medium
🇱🇷 LiberiaLR44.66🟡 Medium
🇷🇸 SerbiaRS44.26🟡 Medium
🇲🇪 MontenegroME43.47🟡 Medium
🇲🇨 MonacoMC43.13🟡 Medium
🇪🇹 EthiopiaET42.85🟡 Medium
🇩🇯 DjiboutiDJ41.96🟡 Medium
🇳🇦 NamibiaNA41.83🟡 Medium
🇱🇦 LaosLA41.73🟡 Medium
🇹🇳 TunisiaTN41.35🟡 Medium
🇬🇮 GibraltarGI41.21🟡 Medium
🇧🇾 BelarusBY41.17🟡 Medium
🇬🇾 GuyanaGY40.75🟡 Medium
🇰🇲 ComorosKM40.50🟡 Medium
🇨🇼 CuracaoCW39.88🟡 Medium
🇵🇰 PakistanPK39.49🟡 Medium
🇸🇱 Sierra LeoneSL39.44🟡 Medium
🇸🇷 SurinameSR39.38🟡 Medium
🇲🇰 North MacedoniaMK39.14🟡 Medium
🇦🇿 AzerbaijanAZ38.69🟡 Medium
🇹🇩 ChadTD38.54🟡 Medium
🇰🇮 KiribatiKI37.94🟡 Medium
🇰🇭 CambodiaKH37.90🟡 Medium
🇬🇶 Equatorial GuineaGQ37.90🟡 Medium
🇸🇨 SeychellesSC37.60🟡 Medium
🇲🇩 MoldovaMD37.54🟡 Medium
🇧🇿 BelizeBZ37.48🟡 Medium
🇭🇰 Hong KongHK37.47🟡 Medium
🇨🇴 ColombiaCO37.26🟡 Medium
🇵🇬 Papua New GuineaPG37.20🟡 Medium
🇸🇿 EswatiniSZ37.00🟡 Medium
🇦🇬 Antigua and BarbudaAG36.74🟡 Medium
🇻🇬 British Virgin IslandsVG36.70🟡 Medium
🇸🇹 Sao Tome and PrincipeST36.64🟡 Medium
🇧🇯 BeninBJ36.60🟡 Medium
🇲🇻 MaldivesMV36.20🟡 Medium
🇱🇨 Saint LuciaLC36.18🟡 Medium
🇰🇳 Saint Kitts and NevisKN36.03🟡 Medium
🇫🇲 MicronesiaFM35.97🟡 Medium
🇹🇻 TuvaluTV35.94🟡 Medium
🇨🇬 CongoCG35.92🟡 Medium
🇹🇯 TajikistanTJ35.90🟡 Medium
🇸🇳 SenegalSN35.88🟡 Medium
🇵🇾 ParaguayPY35.77🟡 Medium
🇹🇭 ThailandTH35.71🟡 Medium
🇼🇸 SamoaWS35.68🟡 Medium
🇬🇦 GabonGA35.54🟡 Medium
🇪🇨 EcuadorEC35.29🟡 Medium
🇭🇳 HondurasHN35.28🟡 Medium
🇸🇻 El SalvadorSV35.15🟡 Medium
🇲🇽 MexicoMX35.01🟡 Medium
🇹🇬 TogoTG35.01🟡 Medium
🇧🇷 BrazilBR34.94🟡 Medium
🇬🇩 GrenadaGD34.87🟡 Medium
🇹🇲 TurkmenistanTM34.75🟡 Medium
🇦🇷 ArgentinaAR34.71🟡 Medium
🇲🇬 MadagascarMG34.67🟡 Medium
🇸🇮 SloveniaSI34.62🟡 Medium
🇳🇵 NepalNP34.60🟡 Medium
🇰🇬 KyrgyzstanKG34.54🟡 Medium
🇹🇱 East TimorTL34.50🟡 Medium
🇮🇩 IndonesiaID34.44🟡 Medium
🇬🇭 GhanaGH34.18🟡 Medium
🇦🇮 AnguillaAI34.17🟡 Medium
🇻🇨 Saint Vincent and the GrenadinesVC34.08🟡 Medium
🇨🇷 Costa RicaCR33.84🟡 Medium
🇧🇴 BoliviaBO33.75🟡 Medium
🇧🇸 The BahamasBS33.48🟡 Medium
🇩🇲 DominicaDM33.16🟡 Medium
🇹🇴 TongaTO33.13🟡 Medium
🇪🇭 Western SaharaEH33.03🟡 Medium
🇵🇪 PeruPE32.99🟡 Medium
🇰🇾 Cayman IslandsKY32.89🟡 Medium
🇨🇾 CyprusCY32.74🟡 Medium
🇦🇲 ArmeniaAM32.69🟡 Medium
🇨🇻 Cape VerdeCV32.61🟡 Medium
🇳🇺 NiueNU32.38🟡 Medium
🇵🇼 PalauPW32.18🟡 Medium
🇲🇾 MalaysiaMY32.16🟡 Medium
🇸🇧 Solomon IslandsSB32.08🟡 Medium
🇮🇳 IndiaIN32.02🟡 Medium
🇮🇱 IsraelIL31.77🟡 Medium
🇬🇲 The GambiaGM31.70🟡 Medium
🇦🇼 ArubaAW31.64🟡 Medium
🇲🇸 MontserratMS31.63🟡 Medium
🇧🇭 BahrainBH31.61🟡 Medium
🇰🇼 KuwaitKW31.52🟡 Medium
🇱🇸 LesothoLS31.50🟡 Medium
🇩🇴 Dominican RepublicDO31.28🟡 Medium
🇪🇬 EgyptEG31.17🟡 Medium
🇲🇭 Marshall IslandsMH31.11🟡 Medium
🇲🇷 MauritaniaMR31.08🟡 Medium
🇹🇨 Turks and CaicosTC31.08🟡 Medium
🇧🇩 BangladeshBD30.99🟡 Medium
🇫🇯 FijiFJ30.88🟡 Medium
🇳🇷 NauruNR30.84🟡 Medium
🇺🇿 UzbekistanUZ30.78🟡 Medium
🇱🇰 Sri LankaLK30.67🟡 Medium
🇲🇦 MoroccoMA30.03🟡 Medium
🇯🇴 JordanJO29.73🟢 Low
🇲🇺 MauritiusMU29.73🟢 Low
🇬🇪 GeorgiaGE29.40🟢 Low
🇰🇿 KazakhstanKZ28.92🟢 Low
🇸🇰 SlovakiaSK28.88🟢 Low
🇲🇳 MongoliaMN28.82🟢 Low
🇷🇼 RwandaRW28.82🟢 Low
🇲🇼 MalawiMW28.76🟢 Low
🇭🇺 HungaryHU28.70🟢 Low
🇷🇴 RomaniaRO28.62🟢 Low
🇲🇹 MaltaMT28.46🟢 Low
🇸🇦 Saudi ArabiaSA28.32🟢 Low
🇿🇲 ZambiaZM28.16🟢 Low
🇨🇰 Cook IslandsCK28.13🟢 Low
🇵🇱 PolandPL28.13🟢 Low
🇴🇲 OmanOM27.90🟢 Low
🇹🇼 TaiwanTW27.90🟢 Low
🇨🇱 ChileCL27.74🟢 Low
🇧🇹 BhutanBT27.70🟢 Low
🇮🇹 ItalyIT27.66🟢 Low
🇧🇼 BotswanaBW27.50🟢 Low
🇧🇶 Caribbean NetherlandsBQ27.23🟢 Low
🇳🇱 NetherlandsNL27.23🟢 Low
🇬🇷 GreeceGR26.70🟢 Low
🇮🇲 Isle of ManIM26.69🟢 Low
🇻🇮 U.S. Virgin IslandsVI26.64🟢 Low
🇮🇪 IrelandIE25.95🟢 Low
🇪🇸 SpainES25.71🟢 Low
🇨🇿 Czech RepublicCZ25.46🟢 Low
🇧🇪 BelgiumBE25.38🟢 Low
🇸🇬 SingaporeSG25.31🟢 Low
🇺🇸 United StatesUS25.22🟢 Low
🇱🇻 LatviaLV25.16🟢 Low
🇲🇴 MacaoMO25.07🟢 Low
🇨🇦 CanadaCA24.95🟢 Low
🇬🇧 United KingdomGB24.79🟢 Low
🇶🇦 QatarQA24.70🟢 Low
🇨🇭 SwitzerlandCH24.51🟢 Low
🇲🇵 Northern Mariana IslandsMP24.19🟢 Low
🇦🇸 American SamoaAS24.10🟢 Low
🇬🇺 GuamGU24.10🟢 Low
🇯🇵 JapanJP23.94🟢 Low
🇩🇪 GermanyDE23.52🟢 Low
🇰🇷 South KoreaKR23.48🟢 Low
🇱🇺 LuxembourgLU23.41🟢 Low
🇵🇹 PortugalPT23.40🟢 Low
🇱🇮 LiechtensteinLI23.23🟢 Low
🇦🇩 AndorraAD22.55🟢 Low
🇦🇹 AustriaAT22.38🟢 Low
🇻🇦 Holy SeeVA22.17🟢 Low
🇦🇺 AustraliaAU22.12🟢 Low
🇵🇷 Puerto RicoPR22.11🟢 Low
🇱🇹 LithuaniaLT22.08🟢 Low
🇺🇾 UruguayUY22.06🟢 Low
🇧🇲 BermudaBM21.85🟢 Low
🇫🇷 FranceFR21.71🟢 Low
🇧🇳 BruneiBN21.40🟢 Low
🇪🇪 EstoniaEE21.37🟢 Low
🇫🇰 Falkland IslandsFK21.20🟢 Low
🇸🇭 Saint HelenaSH21.20🟢 Low
🇬🇫 French GuianaGF21.05🟢 Low
🇵🇫 French PolynesiaPF21.05🟢 Low
🇬🇵 GuadeloupeGP21.05🟢 Low
🇲🇶 MartiniqueMQ21.05🟢 Low
🇳🇨 New CaledoniaNC21.05🟢 Low
🇧🇱 Saint BarthelemyBL21.05🟢 Low
🇲🇫 Saint MartinMF21.05🟢 Low
🇵🇲 Saint Pierre and MiquelonPM21.05🟢 Low
🇼🇫 Wallis and FutunaWF21.05🟢 Low
🇮🇸 IcelandIS21.04🟢 Low
🇬🇱 GreenlandGL19.43🟢 Low
🇳🇿 New ZealandNZ19.19🟢 Low
🇹🇰 TokelauTK19.18🟢 Low
🇸🇲 San MarinoSM19.10🟢 Low
🇸🇪 SwedenSE18.85🟢 Low
🇩🇰 DenmarkDK18.46🟢 Low
🇫🇴 Faroe IslandsFO18.46🟢 Low
🇫🇮 FinlandFI18.01🟢 Low
🇳🇴 NorwayNO17.62🟢 Low

233 countries · Risk scores based on AML/CFT factors


Category Score (50% Weight)

The category score reflects the inherent risk level based on the type of watchlist the entity appears on. This is the most heavily weighted component because the listing type directly indicates regulatory and compliance risk.
CategoryScoreDescription
Sanctions100Listed on international sanctions lists (OFAC, UN, EU, etc.)
PEP100Politically Exposed Person (general classification)
PEP Level 1100Heads of state, senior government officials, supreme court judges
Warnings and Regulatory95Regulatory warnings, enforcement actions
Insolvency80Bankruptcy, liquidation proceedings
PEP Level 280Members of parliament, senior military officers, ambassadors
SIE75State-Influenced Enterprise — companies with government ties
SIP75State-Influenced Person — individuals with government influence
PEP Level 370Regional government officials, local mayors, judges
Fitness and Probity65Financial services fitness assessments, regulatory reviews
Adverse Media60Negative news coverage related to financial crime
PEP Level 455Lower-level officials, political party members
Businessperson55High-profile business figures with potential exposure
Business40General business entities with watchlist presence
Note: When a hit belongs to multiple categories, the highest category score is used.

Category Risk Tiers

┌─────────────────────────────────────────────────────────────────┐
│  CRITICAL (100)     │ Sanctions, PEP, PEP Level 1              │
├─────────────────────────────────────────────────────────────────┤
│  VERY HIGH (80-95)  │ Warnings, Insolvency, PEP Level 2        │
├─────────────────────────────────────────────────────────────────┤
│  HIGH (70-79)       │ SIE, SIP, PEP Level 3                    │
├─────────────────────────────────────────────────────────────────┤
│  ELEVATED (55-69)   │ Fitness/Probity, Adverse Media, PEP L4   │
├─────────────────────────────────────────────────────────────────┤
│  MODERATE (40-54)   │ Business, Businessperson                 │
└─────────────────────────────────────────────────────────────────┘

Criminal Records Score (20% Weight)

The criminal records component evaluates any criminal history associated with the entity.
Criminal StatusScoreDescription
Convicted by court100Entity has been convicted of a criminal offense
Criminal penalty enforced90Entity has had criminal penalties or fines enforced
No criminal records0No known criminal history
Note: Criminal records carry significant weight despite the 20% allocation because a conviction can dramatically change the risk profile of an entity.

Configuration Options

Risk Score Thresholds (Determine Final AML Status)

SettingDefaultDescription
Approve Threshold80Risk scores below this result in Approved status
Review Threshold100Risk scores above this result in Declined status. Scores between thresholds result in In Review status.

Match Score Threshold (Determine Hit Classification)

SettingDefaultDescription
Match Score Threshold93Match scores below this classify hits as False Positive. Only non-false-positive hits are included in risk assessment.

Complete Flow: From Screening to Final Status

┌─────────────────────────────────────────────────────────────────────────────┐
│                           AML SCREENING FLOW                                 │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  1. SCREEN AGAINST WATCHLISTS                                               │
│     └── Returns list of potential hits (match score ≥ 75%)                  │
│                                                                             │
│  2. FOR EACH HIT: CALCULATE MATCH SCORE                                     │
│     Match Score = (Name × W1) + (DOB × W2) + (Country × W3)                │
│     + Golden Key document number logic                                      │
│                                                                             │
│  3. CLASSIFY HITS BY MATCH SCORE                                            │
│     ├── Match Score < 93 → FALSE POSITIVE (excluded)                        │
│     └── Match Score ≥ 93 → UNREVIEWED (included)                            │
│                                                                             │
│  4. FOR EACH UNREVIEWED HIT: CALCULATE RISK SCORE                           │
│     Risk Score = (Country × 30%) + (Category × 50%) + (Criminal × 20%)     │
│                                                                             │
│  5. DETERMINE FINAL AML STATUS                                              │
│     If no non-false-positive hits → APPROVED                                │
│     Else, based on HIGHEST RISK SCORE:                                      │
│     ├── Risk Score < 86 → APPROVED                                          │
│     ├── 86 ≤ Risk Score ≤ 100 → IN REVIEW                                   │
│     └── Risk Score > 100 → DECLINED                                         │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

Calculation Examples

Example 1: High-Risk PEP from Sanctioned Country

Hit Data:
  • Country: Iran (Score: 81.66)
  • Category: Sanctions (Score: 100)
  • Criminal Records: None (Score: 0)
Risk Score Calculation:
Risk Score = (81.66 × 0.30) + (100 × 0.50) + (0 × 0.20)
           = 24.50 + 50.00 + 0.00
           = 74.50
Result: Risk Score 74.50 → Entity Risk Level: High 🔴
  • If this is the highest risk score among unreviewed hits and thresholds are default (86/100):
  • 74.50 < 86 → Final AML Status: Approved (entity is risky but below review threshold)

Example 2: PEP Level 1 from High-Risk Country

Hit Data:
  • Country: Russia (Score: 71.25)
  • Category: PEP Level 1 (Score: 100)
  • Criminal Records: Convicted by court (Score: 100)
Risk Score Calculation:
Risk Score = (71.25 × 0.30) + (100 × 0.50) + (100 × 0.20)
           = 21.38 + 50.00 + 20.00
           = 91.38
Result: Risk Score 91.38 → Entity Risk Level: High 🔴
  • With default thresholds (86/100): 86 ≤ 91.38 ≤ 100 → Final AML Status: In Review

Example 3: Low-Risk Business Entity

Hit Data:
  • Country: United Kingdom (Score: 24.79)
  • Category: Business (Score: 40)
  • Criminal Records: None (Score: 0)
Risk Score Calculation:
Risk Score = (24.79 × 0.30) + (40 × 0.50) + (0 × 0.20)
           = 7.44 + 20.00 + 0.00
           = 27.44
Result: Risk Score 27.44 → Entity Risk Level: Low 🟢
  • With default thresholds (86/100): 27.44 < 86 → Final AML Status: Approved

API Response Example

Each hit in the AML response includes both scores:
{
  "caption": "John Doe",
  "match_score": 95,
  "risk_score": 74.5,
  "review_status": "Unreviewed",
  "properties": {
    "countries": ["IR"],
    "topics": ["sanction"]
  }
}
The overall AML result includes the final status based on the highest risk score:
{
  "status": "In Review",
  "total_hits": 3,
  "score": 91,
  "hits": [
    {
      "caption": "John Doe",
      "match_score": 95,
      "risk_score": 91,
      "review_status": "Unreviewed"
    },
    {
      "caption": "Johnny D.",
      "match_score": 88,
      "risk_score": 45,
      "review_status": "False Positive"
    },
    {
      "caption": "J. Doe",
      "match_score": 94,
      "risk_score": 65,
      "review_status": "Unreviewed"
    }
  ]
}
In this example:
  • Hit 2 is a False Positive (match_score 88 < threshold 93), so it’s excluded
  • The highest risk_score among unreviewed hits is 91 (from Hit 1)
  • 86 ≤ 91 ≤ 100 → Final status: In Review

Managing Hit Review Status

You can manually update each hit’s review status after the initial classification:
StatusDescription
UnreviewedInitial status for hits with match score ≥ threshold
False PositiveInitial status for hits with match score < threshold (or manually marked)
Confirmed MatchManually verified as the same person
InconclusiveUnable to determine if the hit is the same person

Tip: Update hit review status in the Console by navigating to the AML overview or clicking on a specific hit’s details.


Best Practices

  1. Understand the two-score system — Match score filters false positives; risk score determines urgency.
  2. Review high-risk hits first — Use risk score to prioritize your compliance team’s workload among unreviewed hits.
  3. Adjust thresholds based on your risk appetite:
    • Lower Match Score Threshold → More hits considered as unreviewed
    • Lower Approve Threshold → More sessions auto-approved
    • Lower Review Threshold → More sessions auto-declined
  4. Document your decisions — When manually reviewing hits, record why you approved or escalated based on both match confidence and risk factors.
  5. Monitor trends — Track your organization’s hit distribution by both match score and risk level to identify patterns.