AML Risk Score

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

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	Risk Score	Risk Level
Iran	81.66	🔴 High
North Korea	78.20	🔴 High
Myanmar	75.09	🔴 High
Afghanistan	74.63	🔴 High
Syria	74.49	🔴 High
Haiti	74.30	🔴 High
Democratic Republic of the Congo	71.66	🔴 High
Russia	71.25	🔴 High
Venezuela	71.09	🔴 High
South Sudan	70.77	🔴 High
Yemen	68.40	🔴 High
Lebanon	67.76	🔴 High
Somalia	65.38	🔴 High
Mali	62.78	🔴 High
Libya	61.96	🔴 High
Mozambique	57.15	🔴 High
Burundi	56.48	🔴 High
Philippines	56.44	🔴 High
Guinea-Bissau	55.99	🔴 High
Kenya	55.49	🔴 High
Kosovo	55.49	🔴 High
Eritrea	54.72	🔴 High
State of Palestine	54.41	🔴 High
Nigeria	54.40	🔴 High
Vietnam	54.24	🔴 High
Iraq	54.15	🔴 High
Tanzania	53.22	🔴 High
Croatia	53.03	🔴 High
Algeria	52.28	🔴 High
Panama	52.28	🔴 High
Cote D'Ivoire	51.61	🔴 High
Cameroon	51.35	🔴 High
Central African Republic	50.98	🔴 High
Sudan	49.52	🔴 High
Angola	49.47	🟡 Medium
Nicaragua	49.07	🟡 Medium
Trinidad and Tobago	48.45	🟡 Medium
Burkina Faso	47.94	🟡 Medium
Turkey	47.93	🟡 Medium
Vanuatu	47.93	🟡 Medium
Niger	47.02	🟡 Medium
Guatemala	47.01	🟡 Medium
Bosnia and Herzegovina	46.75	🟡 Medium
Uganda	46.67	🟡 Medium
South Africa	46.60	🟡 Medium
Ukraine	46.43	🟡 Medium
China	45.92	🟡 Medium
Cuba	45.89	🟡 Medium
Bulgaria	45.70	🟡 Medium
United Arab Emirates	45.39	🟡 Medium
Zimbabwe	45.37	🟡 Medium
Albania	45.34	🟡 Medium
Jamaica	45.11	🟡 Medium
Barbados	45.04	🟡 Medium
Guinea	44.80	🟡 Medium
Sint Maarten	44.79	🟡 Medium
Liberia	44.66	🟡 Medium
Serbia	44.26	🟡 Medium
Montenegro	43.47	🟡 Medium
Monaco	43.13	🟡 Medium
Ethiopia	42.85	🟡 Medium
Djibouti	41.96	🟡 Medium
Namibia	41.83	🟡 Medium
Laos	41.73	🟡 Medium
Tunisia	41.35	🟡 Medium
Gibraltar	41.21	🟡 Medium
Belarus	41.17	🟡 Medium
Guyana	40.75	🟡 Medium
Comoros	40.50	🟡 Medium
Curacao	39.88	🟡 Medium
Pakistan	39.49	🟡 Medium
Sierra Leone	39.44	🟡 Medium
Suriname	39.38	🟡 Medium
North Macedonia	39.14	🟡 Medium
Azerbaijan	38.69	🟡 Medium
Chad	38.54	🟡 Medium
Kiribati	37.94	🟡 Medium
Cambodia	37.90	🟡 Medium
Equatorial Guinea	37.90	🟡 Medium
Seychelles	37.60	🟡 Medium
Moldova	37.54	🟡 Medium
Belize	37.48	🟡 Medium
Hong Kong	37.47	🟡 Medium
Colombia	37.26	🟡 Medium
Papua New Guinea	37.20	🟡 Medium
Eswatini	37.00	🟡 Medium
Antigua and Barbuda	36.74	🟡 Medium
British Virgin Islands	36.70	🟡 Medium
Sao Tome and Principe	36.64	🟡 Medium
Benin	36.60	🟡 Medium
Maldives	36.20	🟡 Medium
Saint Lucia	36.18	🟡 Medium
Saint Kitts and Nevis	36.03	🟡 Medium
Micronesia	35.97	🟡 Medium
Tuvalu	35.94	🟡 Medium
Congo	35.92	🟡 Medium
Tajikistan	35.90	🟡 Medium
Senegal	35.88	🟡 Medium
Paraguay	35.77	🟡 Medium
Thailand	35.71	🟡 Medium
Samoa	35.68	🟡 Medium
Gabon	35.54	🟡 Medium
Ecuador	35.29	🟡 Medium
Honduras	35.28	🟡 Medium
El Salvador	35.15	🟡 Medium
Mexico	35.01	🟡 Medium
Togo	35.01	🟡 Medium
Brazil	34.94	🟡 Medium
Grenada	34.87	🟡 Medium
Turkmenistan	34.75	🟡 Medium
Argentina	34.71	🟡 Medium
Madagascar	34.67	🟡 Medium
Slovenia	34.62	🟡 Medium
Nepal	34.60	🟡 Medium
Kyrgyzstan	34.54	🟡 Medium
East Timor	34.50	🟡 Medium
Indonesia	34.44	🟡 Medium
Ghana	34.18	🟡 Medium
Anguilla	34.17	🟡 Medium
Saint Vincent and the Grenadines	34.08	🟡 Medium
Costa Rica	33.84	🟡 Medium
Bolivia	33.75	🟡 Medium
The Bahamas	33.48	🟡 Medium
Dominica	33.16	🟡 Medium
Tonga	33.13	🟡 Medium
Western Sahara	33.03	🟡 Medium
Peru	32.99	🟡 Medium
Cayman Islands	32.89	🟡 Medium
Cyprus	32.74	🟡 Medium
Armenia	32.69	🟡 Medium
Cape Verde	32.61	🟡 Medium
Niue	32.38	🟡 Medium
Palau	32.18	🟡 Medium
Malaysia	32.16	🟡 Medium
Solomon Islands	32.08	🟡 Medium
India	32.02	🟡 Medium
Israel	31.77	🟡 Medium
The Gambia	31.70	🟡 Medium
Aruba	31.64	🟡 Medium
Montserrat	31.63	🟡 Medium
Bahrain	31.61	🟡 Medium
Kuwait	31.52	🟡 Medium
Lesotho	31.50	🟡 Medium
Dominican Republic	31.28	🟡 Medium
Egypt	31.17	🟡 Medium
Marshall Islands	31.11	🟡 Medium
Mauritania	31.08	🟡 Medium
Turks and Caicos	31.08	🟡 Medium
Bangladesh	30.99	🟡 Medium
Fiji	30.88	🟡 Medium
Nauru	30.84	🟡 Medium
Uzbekistan	30.78	🟡 Medium
Sri Lanka	30.67	🟡 Medium
Morocco	30.03	🟡 Medium
Jordan	29.73	🟢 Low
Mauritius	29.73	🟢 Low
Georgia	29.40	🟢 Low
Kazakhstan	28.92	🟢 Low
Slovakia	28.88	🟢 Low
Mongolia	28.82	🟢 Low
Rwanda	28.82	🟢 Low
Malawi	28.76	🟢 Low
Hungary	28.70	🟢 Low
Romania	28.62	🟢 Low
Malta	28.46	🟢 Low
Saudi Arabia	28.32	🟢 Low
Zambia	28.16	🟢 Low
Cook Islands	28.13	🟢 Low
Poland	28.13	🟢 Low
Oman	27.90	🟢 Low
Taiwan	27.90	🟢 Low
Chile	27.74	🟢 Low
Bhutan	27.70	🟢 Low
Italy	27.66	🟢 Low
Botswana	27.50	🟢 Low
Caribbean Netherlands	27.23	🟢 Low
Netherlands	27.23	🟢 Low
Greece	26.70	🟢 Low
Isle of Man	26.69	🟢 Low
U.S. Virgin Islands	26.64	🟢 Low
Ireland	25.95	🟢 Low
Spain	25.71	🟢 Low
Czech Republic	25.46	🟢 Low
Belgium	25.38	🟢 Low
Singapore	25.31	🟢 Low
United States	25.22	🟢 Low
Latvia	25.16	🟢 Low
Macao	25.07	🟢 Low
Canada	24.95	🟢 Low
United Kingdom	24.79	🟢 Low
Qatar	24.70	🟢 Low
Switzerland	24.51	🟢 Low
Northern Mariana Islands	24.19	🟢 Low
American Samoa	24.10	🟢 Low
Guam	24.10	🟢 Low
Japan	23.94	🟢 Low
Germany	23.52	🟢 Low
South Korea	23.48	🟢 Low
Luxembourg	23.41	🟢 Low
Portugal	23.40	🟢 Low
Liechtenstein	23.23	🟢 Low
Andorra	22.55	🟢 Low
Austria	22.38	🟢 Low
Holy See	22.17	🟢 Low
Australia	22.12	🟢 Low
Puerto Rico	22.11	🟢 Low
Lithuania	22.08	🟢 Low
Uruguay	22.06	🟢 Low
Bermuda	21.85	🟢 Low
France	21.71	🟢 Low
Brunei	21.40	🟢 Low
Estonia	21.37	🟢 Low
Falkland Islands	21.20	🟢 Low
Saint Helena	21.20	🟢 Low
French Guiana	21.05	🟢 Low
French Polynesia	21.05	🟢 Low
Guadeloupe	21.05	🟢 Low
Martinique	21.05	🟢 Low
New Caledonia	21.05	🟢 Low
Saint Barthelemy	21.05	🟢 Low
Saint Martin	21.05	🟢 Low
Saint Pierre and Miquelon	21.05	🟢 Low
Wallis and Futuna	21.05	🟢 Low
Iceland	21.04	🟢 Low
Greenland	19.43	🟢 Low
New Zealand	19.19	🟢 Low
Tokelau	19.18	🟢 Low
San Marino	19.10	🟢 Low
Sweden	18.85	🟢 Low
Denmark	18.46	🟢 Low
Faroe Islands	18.46	🟢 Low
Finland	18.01	🟢 Low
Norway	17.62	🟢 Low

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:

{
  "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:

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

Understand the two-score system — Match score filters false positives; risk score determines urgency.
Review high-risk hits first — Use risk score to prioritize your compliance team’s workload among unreviewed hits.
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
Document your decisions — When manually reviewing hits, record why you approved or escalated based on both match confidence and risk factors.
Monitor trends — Track your organization’s hit distribution by both match score and risk level to identify patterns.

ID Verification

Liveness Detection

Face Match (1:1)

Face Search (1:N)

Age Estimation

AML Screening

Database Validation

Email Verification

Phone Verification

Proof of Address

NFC Verification

IP Analysis

Biometric Authentication

Questionnaires

Reusable KYC

Overview

Risk Score vs Match Score

How Risk Score Determines Final AML Status

Risk Score Formula

Risk Levels

Component Scoring Details

Country Score (30% Weight)

Country Risk Scores

Category Score (50% Weight)

Category Risk Tiers

Criminal Records Score (20% Weight)

Configuration Options

Risk Score Thresholds (Determine Final AML Status)

Match Score Threshold (Determine Hit Classification)

Complete Flow: From Screening to Final Status

Calculation Examples

Example 1: High-Risk PEP from Sanctioned Country

Example 2: PEP Level 1 from High-Risk Country

Example 3: Low-Risk Business Entity

API Response Example

Managing Hit Review Status

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

Best Practices

ID Verification

Liveness Detection

Face Match (1:1)

Face Search (1:N)

Age Estimation

AML Screening

Database Validation

Email Verification

Phone Verification

Proof of Address

NFC Verification

IP Analysis

Biometric Authentication

Questionnaires

Reusable KYC

​Overview

​Risk Score vs Match Score

​How Risk Score Determines Final AML Status

​Risk Score Formula

​Risk Levels

​Component Scoring Details

​Country Score (30% Weight)

​Country Risk Scores

​Category Score (50% Weight)

​Category Risk Tiers

​Criminal Records Score (20% Weight)

​Configuration Options

​Risk Score Thresholds (Determine Final AML Status)

​Match Score Threshold (Determine Hit Classification)

​Complete Flow: From Screening to Final Status

​Calculation Examples

​Example 1: High-Risk PEP from Sanctioned Country

​Example 2: PEP Level 1 from High-Risk Country

​Example 3: Low-Risk Business Entity

​API Response Example

​Managing Hit Review Status

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

​Best Practices

Overview

Risk Score vs Match Score

How Risk Score Determines Final AML Status

Risk Score Formula

Risk Levels

Component Scoring Details

Country Score (30% Weight)

Country Risk Scores

Category Score (50% Weight)

Category Risk Tiers

Criminal Records Score (20% Weight)

Configuration Options

Risk Score Thresholds (Determine Final AML Status)

Match Score Threshold (Determine Hit Classification)

Complete Flow: From Screening to Final Status

Calculation Examples

Example 1: High-Risk PEP from Sanctioned Country

Example 2: PEP Level 1 from High-Risk Country

Example 3: Low-Risk Business Entity

API Response Example

Managing Hit Review Status

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

Best Practices