> ## 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.
# AML Risk Score
> Understand how AML risk scores are calculated from country, category, and criminal record factors. Tune thresholds to automate AML decisions.
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](/core-technology/aml-screening/aml-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
| Aspect | Match Score | Risk Score |
| -------------- | --------------------------------------------- | ----------------------------------- |
| **Question** | Is this the same person? | How risky is this entity? |
| **Purpose** | Classify hits as False Positive vs Unreviewed | Determine final AML status |
| **Factors** | Name, DOB, Country, Document Number | Country, Category, Criminal Records |
| **Applied to** | All hits | Only 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 Score | Final AML Status |
| ------------------------------------------- | ---------------- |
| Score below Approve Threshold | **Approved** |
| Score between Approve and Review Thresholds | **In Review** |
| Score above Review Threshold | **Declined** |
> **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)
```
| Component | Weight | Description |
| -------------------- | ------ | --------------------------------------------------- |
| **Country** | 30% | Geographic risk assessment based on AML/CFT factors |
| **Category** | 50% | Risk level based on the type of watchlist listing |
| **Criminal Records** | 20% | Risk from criminal history and convictions |
***
## Risk Levels
Based on the calculated risk score, entities are classified into three risk tiers:
| Risk Level | Score Range | Description |
| ------------------ | ----------- | ---------------------------------------------------- |
| 🟢 **Low Risk** | \< 30 | Minimal compliance concern, standard due diligence |
| 🟡 **Medium Risk** | 30 – 49 | Elevated concern, enhanced due diligence recommended |
| 🔴 **High Risk** | ≥ 50 | Significant 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
| Country | Code | Risk Score | Risk Level |
| ------------------------------------- | ---- | ---------- | ---------- |
| 🇮🇷 Iran | `IR` | 81.66 | 🔴 High |
| 🇰🇵 North Korea | `KP` | 78.20 | 🔴 High |
| 🇲🇲 Myanmar | `MM` | 75.09 | 🔴 High |
| 🇦🇫 Afghanistan | `AF` | 74.63 | 🔴 High |
| 🇸🇾 Syria | `SY` | 74.49 | 🔴 High |
| 🇭🇹 Haiti | `HT` | 74.30 | 🔴 High |
| 🇨🇩 Democratic Republic of the Congo | `CD` | 71.66 | 🔴 High |
| 🇷🇺 Russia | `RU` | 71.25 | 🔴 High |
| 🇻🇪 Venezuela | `VE` | 71.09 | 🔴 High |
| 🇸🇸 South Sudan | `SS` | 70.77 | 🔴 High |
| 🇾🇪 Yemen | `YE` | 68.40 | 🔴 High |
| 🇱🇧 Lebanon | `LB` | 67.76 | 🔴 High |
| 🇸🇴 Somalia | `SO` | 65.38 | 🔴 High |
| 🇲🇱 Mali | `ML` | 62.78 | 🔴 High |
| 🇱🇾 Libya | `LY` | 61.96 | 🔴 High |
| 🇲🇿 Mozambique | `MZ` | 57.15 | 🔴 High |
| 🇧🇮 Burundi | `BI` | 56.48 | 🔴 High |
| 🇵🇭 Philippines | `PH` | 56.44 | 🔴 High |
| 🇬🇼 Guinea-Bissau | `GW` | 55.99 | 🔴 High |
| 🇰🇪 Kenya | `KE` | 55.49 | 🔴 High |
| 🇽🇰 Kosovo | `XK` | 55.49 | 🔴 High |
| 🇪🇷 Eritrea | `ER` | 54.72 | 🔴 High |
| 🇵🇸 State of Palestine | `PS` | 54.41 | 🔴 High |
| 🇳🇬 Nigeria | `NG` | 54.40 | 🔴 High |
| 🇻🇳 Vietnam | `VN` | 54.24 | 🔴 High |
| 🇮🇶 Iraq | `IQ` | 54.15 | 🔴 High |
| 🇹🇿 Tanzania | `TZ` | 53.22 | 🔴 High |
| 🇭🇷 Croatia | `HR` | 53.03 | 🔴 High |
| 🇩🇿 Algeria | `DZ` | 52.28 | 🔴 High |
| 🇵🇦 Panama | `PA` | 52.28 | 🔴 High |
| 🇨🇮 Cote D'Ivoire | `CI` | 51.61 | 🔴 High |
| 🇨🇲 Cameroon | `CM` | 51.35 | 🔴 High |
| 🇨🇫 Central African Republic | `CF` | 50.98 | 🔴 High |
| 🇸🇩 Sudan | `SD` | 49.52 | 🔴 High |
| 🇦🇴 Angola | `AO` | 49.47 | 🟡 Medium |
| 🇳🇮 Nicaragua | `NI` | 49.07 | 🟡 Medium |
| 🇹🇹 Trinidad and Tobago | `TT` | 48.45 | 🟡 Medium |
| 🇧🇫 Burkina Faso | `BF` | 47.94 | 🟡 Medium |
| 🇹🇷 Turkey | `TR` | 47.93 | 🟡 Medium |
| 🇻🇺 Vanuatu | `VU` | 47.93 | 🟡 Medium |
| 🇳🇪 Niger | `NE` | 47.02 | 🟡 Medium |
| 🇬🇹 Guatemala | `GT` | 47.01 | 🟡 Medium |
| 🇧🇦 Bosnia and Herzegovina | `BA` | 46.75 | 🟡 Medium |
| 🇺🇬 Uganda | `UG` | 46.67 | 🟡 Medium |
| 🇿🇦 South Africa | `ZA` | 46.60 | 🟡 Medium |
| 🇺🇦 Ukraine | `UA` | 46.43 | 🟡 Medium |
| 🇨🇳 China | `CN` | 45.92 | 🟡 Medium |
| 🇨🇺 Cuba | `CU` | 45.89 | 🟡 Medium |
| 🇧🇬 Bulgaria | `BG` | 45.70 | 🟡 Medium |
| 🇦🇪 United Arab Emirates | `AE` | 45.39 | 🟡 Medium |
| 🇿🇼 Zimbabwe | `ZW` | 45.37 | 🟡 Medium |
| 🇦🇱 Albania | `AL` | 45.34 | 🟡 Medium |
| 🇯🇲 Jamaica | `JM` | 45.11 | 🟡 Medium |
| 🇧🇧 Barbados | `BB` | 45.04 | 🟡 Medium |
| 🇬🇳 Guinea | `GN` | 44.80 | 🟡 Medium |
| 🇸🇽 Sint Maarten | `SX` | 44.79 | 🟡 Medium |
| 🇱🇷 Liberia | `LR` | 44.66 | 🟡 Medium |
| 🇷🇸 Serbia | `RS` | 44.26 | 🟡 Medium |
| 🇲🇪 Montenegro | `ME` | 43.47 | 🟡 Medium |
| 🇲🇨 Monaco | `MC` | 43.13 | 🟡 Medium |
| 🇪🇹 Ethiopia | `ET` | 42.85 | 🟡 Medium |
| 🇩🇯 Djibouti | `DJ` | 41.96 | 🟡 Medium |
| 🇳🇦 Namibia | `NA` | 41.83 | 🟡 Medium |
| 🇱🇦 Laos | `LA` | 41.73 | 🟡 Medium |
| 🇹🇳 Tunisia | `TN` | 41.35 | 🟡 Medium |
| 🇬🇮 Gibraltar | `GI` | 41.21 | 🟡 Medium |
| 🇧🇾 Belarus | `BY` | 41.17 | 🟡 Medium |
| 🇬🇾 Guyana | `GY` | 40.75 | 🟡 Medium |
| 🇰🇲 Comoros | `KM` | 40.50 | 🟡 Medium |
| 🇨🇼 Curacao | `CW` | 39.88 | 🟡 Medium |
| 🇵🇰 Pakistan | `PK` | 39.49 | 🟡 Medium |
| 🇸🇱 Sierra Leone | `SL` | 39.44 | 🟡 Medium |
| 🇸🇷 Suriname | `SR` | 39.38 | 🟡 Medium |
| 🇲🇰 North Macedonia | `MK` | 39.14 | 🟡 Medium |
| 🇦🇿 Azerbaijan | `AZ` | 38.69 | 🟡 Medium |
| 🇹🇩 Chad | `TD` | 38.54 | 🟡 Medium |
| 🇰🇮 Kiribati | `KI` | 37.94 | 🟡 Medium |
| 🇰🇭 Cambodia | `KH` | 37.90 | 🟡 Medium |
| 🇬🇶 Equatorial Guinea | `GQ` | 37.90 | 🟡 Medium |
| 🇸🇨 Seychelles | `SC` | 37.60 | 🟡 Medium |
| 🇲🇩 Moldova | `MD` | 37.54 | 🟡 Medium |
| 🇧🇿 Belize | `BZ` | 37.48 | 🟡 Medium |
| 🇭🇰 Hong Kong | `HK` | 37.47 | 🟡 Medium |
| 🇨🇴 Colombia | `CO` | 37.26 | 🟡 Medium |
| 🇵🇬 Papua New Guinea | `PG` | 37.20 | 🟡 Medium |
| 🇸🇿 Eswatini | `SZ` | 37.00 | 🟡 Medium |
| 🇦🇬 Antigua and Barbuda | `AG` | 36.74 | 🟡 Medium |
| 🇻🇬 British Virgin Islands | `VG` | 36.70 | 🟡 Medium |
| 🇸🇹 Sao Tome and Principe | `ST` | 36.64 | 🟡 Medium |
| 🇧🇯 Benin | `BJ` | 36.60 | 🟡 Medium |
| 🇲🇻 Maldives | `MV` | 36.20 | 🟡 Medium |
| 🇱🇨 Saint Lucia | `LC` | 36.18 | 🟡 Medium |
| 🇰🇳 Saint Kitts and Nevis | `KN` | 36.03 | 🟡 Medium |
| 🇫🇲 Micronesia | `FM` | 35.97 | 🟡 Medium |
| 🇹🇻 Tuvalu | `TV` | 35.94 | 🟡 Medium |
| 🇨🇬 Congo | `CG` | 35.92 | 🟡 Medium |
| 🇹🇯 Tajikistan | `TJ` | 35.90 | 🟡 Medium |
| 🇸🇳 Senegal | `SN` | 35.88 | 🟡 Medium |
| 🇵🇾 Paraguay | `PY` | 35.77 | 🟡 Medium |
| 🇹🇭 Thailand | `TH` | 35.71 | 🟡 Medium |
| 🇼🇸 Samoa | `WS` | 35.68 | 🟡 Medium |
| 🇬🇦 Gabon | `GA` | 35.54 | 🟡 Medium |
| 🇪🇨 Ecuador | `EC` | 35.29 | 🟡 Medium |
| 🇭🇳 Honduras | `HN` | 35.28 | 🟡 Medium |
| 🇸🇻 El Salvador | `SV` | 35.15 | 🟡 Medium |
| 🇲🇽 Mexico | `MX` | 35.01 | 🟡 Medium |
| 🇹🇬 Togo | `TG` | 35.01 | 🟡 Medium |
| 🇧🇷 Brazil | `BR` | 34.94 | 🟡 Medium |
| 🇬🇩 Grenada | `GD` | 34.87 | 🟡 Medium |
| 🇹🇲 Turkmenistan | `TM` | 34.75 | 🟡 Medium |
| 🇦🇷 Argentina | `AR` | 34.71 | 🟡 Medium |
| 🇲🇬 Madagascar | `MG` | 34.67 | 🟡 Medium |
| 🇸🇮 Slovenia | `SI` | 34.62 | 🟡 Medium |
| 🇳🇵 Nepal | `NP` | 34.60 | 🟡 Medium |
| 🇰🇬 Kyrgyzstan | `KG` | 34.54 | 🟡 Medium |
| 🇹🇱 East Timor | `TL` | 34.50 | 🟡 Medium |
| 🇮🇩 Indonesia | `ID` | 34.44 | 🟡 Medium |
| 🇬🇭 Ghana | `GH` | 34.18 | 🟡 Medium |
| 🇦🇮 Anguilla | `AI` | 34.17 | 🟡 Medium |
| 🇻🇨 Saint Vincent and the Grenadines | `VC` | 34.08 | 🟡 Medium |
| 🇨🇷 Costa Rica | `CR` | 33.84 | 🟡 Medium |
| 🇧🇴 Bolivia | `BO` | 33.75 | 🟡 Medium |
| 🇧🇸 The Bahamas | `BS` | 33.48 | 🟡 Medium |
| 🇩🇲 Dominica | `DM` | 33.16 | 🟡 Medium |
| 🇹🇴 Tonga | `TO` | 33.13 | 🟡 Medium |
| 🇪🇭 Western Sahara | `EH` | 33.03 | 🟡 Medium |
| 🇵🇪 Peru | `PE` | 32.99 | 🟡 Medium |
| 🇰🇾 Cayman Islands | `KY` | 32.89 | 🟡 Medium |
| 🇨🇾 Cyprus | `CY` | 32.74 | 🟡 Medium |
| 🇦🇲 Armenia | `AM` | 32.69 | 🟡 Medium |
| 🇨🇻 Cape Verde | `CV` | 32.61 | 🟡 Medium |
| 🇳🇺 Niue | `NU` | 32.38 | 🟡 Medium |
| 🇵🇼 Palau | `PW` | 32.18 | 🟡 Medium |
| 🇲🇾 Malaysia | `MY` | 32.16 | 🟡 Medium |
| 🇸🇧 Solomon Islands | `SB` | 32.08 | 🟡 Medium |
| 🇮🇳 India | `IN` | 32.02 | 🟡 Medium |
| 🇮🇱 Israel | `IL` | 31.77 | 🟡 Medium |
| 🇬🇲 The Gambia | `GM` | 31.70 | 🟡 Medium |
| 🇦🇼 Aruba | `AW` | 31.64 | 🟡 Medium |
| 🇲🇸 Montserrat | `MS` | 31.63 | 🟡 Medium |
| 🇧🇭 Bahrain | `BH` | 31.61 | 🟡 Medium |
| 🇰🇼 Kuwait | `KW` | 31.52 | 🟡 Medium |
| 🇱🇸 Lesotho | `LS` | 31.50 | 🟡 Medium |
| 🇩🇴 Dominican Republic | `DO` | 31.28 | 🟡 Medium |
| 🇪🇬 Egypt | `EG` | 31.17 | 🟡 Medium |
| 🇲🇭 Marshall Islands | `MH` | 31.11 | 🟡 Medium |
| 🇲🇷 Mauritania | `MR` | 31.08 | 🟡 Medium |
| 🇹🇨 Turks and Caicos | `TC` | 31.08 | 🟡 Medium |
| 🇧🇩 Bangladesh | `BD` | 30.99 | 🟡 Medium |
| 🇫🇯 Fiji | `FJ` | 30.88 | 🟡 Medium |
| 🇳🇷 Nauru | `NR` | 30.84 | 🟡 Medium |
| 🇺🇿 Uzbekistan | `UZ` | 30.78 | 🟡 Medium |
| 🇱🇰 Sri Lanka | `LK` | 30.67 | 🟡 Medium |
| 🇲🇦 Morocco | `MA` | 30.03 | 🟡 Medium |
| 🇯🇴 Jordan | `JO` | 29.73 | 🟢 Low |
| 🇲🇺 Mauritius | `MU` | 29.73 | 🟢 Low |
| 🇬🇪 Georgia | `GE` | 29.40 | 🟢 Low |
| 🇰🇿 Kazakhstan | `KZ` | 28.92 | 🟢 Low |
| 🇸🇰 Slovakia | `SK` | 28.88 | 🟢 Low |
| 🇲🇳 Mongolia | `MN` | 28.82 | 🟢 Low |
| 🇷🇼 Rwanda | `RW` | 28.82 | 🟢 Low |
| 🇲🇼 Malawi | `MW` | 28.76 | 🟢 Low |
| 🇭🇺 Hungary | `HU` | 28.70 | 🟢 Low |
| 🇷🇴 Romania | `RO` | 28.62 | 🟢 Low |
| 🇲🇹 Malta | `MT` | 28.46 | 🟢 Low |
| 🇸🇦 Saudi Arabia | `SA` | 28.32 | 🟢 Low |
| 🇿🇲 Zambia | `ZM` | 28.16 | 🟢 Low |
| 🇨🇰 Cook Islands | `CK` | 28.13 | 🟢 Low |
| 🇵🇱 Poland | `PL` | 28.13 | 🟢 Low |
| 🇴🇲 Oman | `OM` | 27.90 | 🟢 Low |
| 🇹🇼 Taiwan | `TW` | 27.90 | 🟢 Low |
| 🇨🇱 Chile | `CL` | 27.74 | 🟢 Low |
| 🇧🇹 Bhutan | `BT` | 27.70 | 🟢 Low |
| 🇮🇹 Italy | `IT` | 27.66 | 🟢 Low |
| 🇧🇼 Botswana | `BW` | 27.50 | 🟢 Low |
| 🇧🇶 Caribbean Netherlands | `BQ` | 27.23 | 🟢 Low |
| 🇳🇱 Netherlands | `NL` | 27.23 | 🟢 Low |
| 🇬🇷 Greece | `GR` | 26.70 | 🟢 Low |
| 🇮🇲 Isle of Man | `IM` | 26.69 | 🟢 Low |
| 🇻🇮 U.S. Virgin Islands | `VI` | 26.64 | 🟢 Low |
| 🇮🇪 Ireland | `IE` | 25.95 | 🟢 Low |
| 🇪🇸 Spain | `ES` | 25.71 | 🟢 Low |
| 🇨🇿 Czech Republic | `CZ` | 25.46 | 🟢 Low |
| 🇧🇪 Belgium | `BE` | 25.38 | 🟢 Low |
| 🇸🇬 Singapore | `SG` | 25.31 | 🟢 Low |
| 🇺🇸 United States | `US` | 25.22 | 🟢 Low |
| 🇱🇻 Latvia | `LV` | 25.16 | 🟢 Low |
| 🇲🇴 Macao | `MO` | 25.07 | 🟢 Low |
| 🇨🇦 Canada | `CA` | 24.95 | 🟢 Low |
| 🇬🇧 United Kingdom | `GB` | 24.79 | 🟢 Low |
| 🇶🇦 Qatar | `QA` | 24.70 | 🟢 Low |
| 🇨🇭 Switzerland | `CH` | 24.51 | 🟢 Low |
| 🇲🇵 Northern Mariana Islands | `MP` | 24.19 | 🟢 Low |
| 🇦🇸 American Samoa | `AS` | 24.10 | 🟢 Low |
| 🇬🇺 Guam | `GU` | 24.10 | 🟢 Low |
| 🇯🇵 Japan | `JP` | 23.94 | 🟢 Low |
| 🇩🇪 Germany | `DE` | 23.52 | 🟢 Low |
| 🇰🇷 South Korea | `KR` | 23.48 | 🟢 Low |
| 🇱🇺 Luxembourg | `LU` | 23.41 | 🟢 Low |
| 🇵🇹 Portugal | `PT` | 23.40 | 🟢 Low |
| 🇱🇮 Liechtenstein | `LI` | 23.23 | 🟢 Low |
| 🇦🇩 Andorra | `AD` | 22.55 | 🟢 Low |
| 🇦🇹 Austria | `AT` | 22.38 | 🟢 Low |
| 🇻🇦 Holy See | `VA` | 22.17 | 🟢 Low |
| 🇦🇺 Australia | `AU` | 22.12 | 🟢 Low |
| 🇵🇷 Puerto Rico | `PR` | 22.11 | 🟢 Low |
| 🇱🇹 Lithuania | `LT` | 22.08 | 🟢 Low |
| 🇺🇾 Uruguay | `UY` | 22.06 | 🟢 Low |
| 🇧🇲 Bermuda | `BM` | 21.85 | 🟢 Low |
| 🇫🇷 France | `FR` | 21.71 | 🟢 Low |
| 🇧🇳 Brunei | `BN` | 21.40 | 🟢 Low |
| 🇪🇪 Estonia | `EE` | 21.37 | 🟢 Low |
| 🇫🇰 Falkland Islands | `FK` | 21.20 | 🟢 Low |
| 🇸🇭 Saint Helena | `SH` | 21.20 | 🟢 Low |
| 🇬🇫 French Guiana | `GF` | 21.05 | 🟢 Low |
| 🇵🇫 French Polynesia | `PF` | 21.05 | 🟢 Low |
| 🇬🇵 Guadeloupe | `GP` | 21.05 | 🟢 Low |
| 🇲🇶 Martinique | `MQ` | 21.05 | 🟢 Low |
| 🇳🇨 New Caledonia | `NC` | 21.05 | 🟢 Low |
| 🇧🇱 Saint Barthelemy | `BL` | 21.05 | 🟢 Low |
| 🇲🇫 Saint Martin | `MF` | 21.05 | 🟢 Low |
| 🇵🇲 Saint Pierre and Miquelon | `PM` | 21.05 | 🟢 Low |
| 🇼🇫 Wallis and Futuna | `WF` | 21.05 | 🟢 Low |
| 🇮🇸 Iceland | `IS` | 21.04 | 🟢 Low |
| 🇬🇱 Greenland | `GL` | 19.43 | 🟢 Low |
| 🇳🇿 New Zealand | `NZ` | 19.19 | 🟢 Low |
| 🇹🇰 Tokelau | `TK` | 19.18 | 🟢 Low |
| 🇸🇲 San Marino | `SM` | 19.10 | 🟢 Low |
| 🇸🇪 Sweden | `SE` | 18.85 | 🟢 Low |
| 🇩🇰 Denmark | `DK` | 18.46 | 🟢 Low |
| 🇫🇴 Faroe Islands | `FO` | 18.46 | 🟢 Low |
| 🇫🇮 Finland | `FI` | 18.01 | 🟢 Low |
| 🇳🇴 Norway | `NO` | 17.62 | 🟢 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.
| Category | Score | Description |
| --------------------------- | ----- | ----------------------------------------------------------------- |
| **Sanctions** | 100 | Listed on international sanctions lists (OFAC, UN, EU, etc.) |
| **PEP** | 100 | Politically Exposed Person (general classification) |
| **PEP Level 1** | 100 | Heads of state, senior government officials, supreme court judges |
| **Warnings and Regulatory** | 95 | Regulatory warnings, enforcement actions |
| **Insolvency** | 80 | Bankruptcy, liquidation proceedings |
| **PEP Level 2** | 80 | Members of parliament, senior military officers, ambassadors |
| **SIE** | 75 | State-Influenced Enterprise — companies with government ties |
| **SIP** | 75 | State-Influenced Person — individuals with government influence |
| **PEP Level 3** | 70 | Regional government officials, local mayors, judges |
| **Fitness and Probity** | 65 | Financial services fitness assessments, regulatory reviews |
| **Adverse Media** | 60 | Negative news coverage related to financial crime |
| **PEP Level 4** | 55 | Lower-level officials, political party members |
| **Businessperson** | 55 | High-profile business figures with potential exposure |
| **Business** | 40 | General 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 Status | Score | Description |
| ----------------------------- | ----- | --------------------------------------------------- |
| **Convicted by court** | 100 | Entity has been convicted of a criminal offense |
| **Criminal penalty enforced** | 90 | Entity has had criminal penalties or fines enforced |
| **No criminal records** | 0 | No 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)
| Setting | Default | Description |
| ----------------- | ------- | --------------------------------------------------------------------------------------------------------------- |
| Approve Threshold | 80 | Risk scores below this result in **Approved** status |
| Review Threshold | 100 | Risk scores above this result in **Declined** status. Scores between thresholds result in **In Review** status. |
### Match Score Threshold (Determine Hit Classification)
| Setting | Default | Description |
| --------------------- | ------- | -------------------------------------------------------------------------------------------------------------------------- |
| Match Score Threshold | 93 | Match 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:
```json theme={null}
{
"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:
```json theme={null}
{
"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:
| Status | Description |
| ------------------- | -------------------------------------------------------------------------- |
| **Unreviewed** | Initial status for hits with match score ≥ threshold |
| **False Positive** | Initial status for hits with match score \< threshold (or manually marked) |
| **Confirmed Match** | Manually verified as the same person |
| **Inconclusive** | Unable 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.