The following table illustrates the mapping between the number of the category and the name of the category.
This information is useful for developers to process the information obtained through the AML API.
This information can be programmatically retrieved using the GET /user/category/list method, which returns the category number (identifier) and the category name.
NUMBER | NAME | RISK | DESCRIPTION EN |
---|---|---|---|
0 | Unmarked | 50 | Unlabeled data |
1 | Darknet Marketplace | 100 | A website with high-risk activities |
2 | Darknet Service | 100 | An individual participant in a high-risk activity |
3 | Drugs | 100 | An individual participant in high-risk drugs activities |
4 | Precursors | 85 | An individual participant in high-risk activities specifically in the area of sale of precursors (goods and substances used in the creation of drugs) |
5 | China precursors manufacturing | 85 | Precursor manufacturer from China |
6 | Child Abuse | 100 | Distribution of prohibited materials involving minors |
7 | Stolen Credit Cards | 100 | Funds received by carders as a result of illegal activities |
8 | Blackmail | 100 | Ransom letters under the guise of hacking a computer or spreading defamatory information |
9 | Personal data RU | 100 | Sale of personal data of citizens of the Russian Federation |
10 | Personal data CIS | 100 | Sale of personal data of citizens of other CIS countries (except the Russian Federation) |
11 | Personal data EU | 100 | Sale of personal data of EU citizens |
12 | Personal data US | 100 | Sale of personal data of US citizens |
13 | Personal data other | 100 | Sale of personal data of citizens of other states |
14 | Stolen DataBase | 100 | Sale of databases (not penetrations, but the databases themselves) |
15 | Smuggling drugs | 100 | Participant in drug smuggling |
16 | Smuggling precursors | 100 | Participant in the smuggling of precursors |
17 | Smuggling people | 100 | Participant in people smuggling |
18 | Smuggling weapons | 100 | weapons smuggler |
19 | Smuggling | 100 | Participant in the smuggling of other goods |
20 | Illegal migration | 100 | Organizer of illegal migration |
21 | Human trafficking | 100 | Human trafficking (without smuggling) |
22 | Fake documents | 100 | Making fake documents (not renderings, but physical documents) |
23 | Fake document rendering | 100 | Manufacture of forged documents (not renderings, but physical documents) |
24 | Illegal weapons | 100 | Sale of weapons |
25 | Laundering of money | 100 | Laundering of proceeds from crime |
26 | Illegal financial transactions | 100 | Illegal financial transactions |
27 | Exchange With High ML Risk | 75 | Cryptocurrency exchange with a high risk of money laundering |
28 | Exchange With Low ML Risk | 25 | Cryptocurrency exchange with a low risk of money laundering |
29 | Exchange With Moderate ML Risk | 50 | Cryptocurrency exchange with an average risk of money laundering |
30 | Exchange With Very High ML Risk | 100 | Cryptocurrency exchange with a very high risk of money laundering |
31 | Fraudulent Exchange | 100 | Fake cryptocurrency exchange (a kind of scam) |
32 | Gambling | 100 | Gambling activity |
33 | Illegal Service | 100 | Other forms of illegal activity not specified in other categories |
34 | Miner | 0 | Miner |
35 | Mixing Service | 100 | Cryptocurrency mixer |
36 | Online Marketplace | 50 | Marketplace |
37 | Online Wallet | 50 | Online cryptocurrency wallet |
38 | PEP | 75 | Politically exposed person |
39 | Corruption | 100 | Corruption |
40 | Bridge | 50 | Cryptocurrency bridge (like renBTC, WBTC, etc.) |
41 | DEX (excluding Bridges) | 50 | Deposit addresses of various DEXs |
42 | P2P Exchange With High ML Risk | 75 | P2P exchange operator with high risk of money laundering |
43 | P2P Exchange With Low ML Risk | 25 | P2P exchange operator with low risk of money laundering |
44 | P2P Exchange With Moderate ML Risk | 50 | P2P exchange operator with medium risk of money laundering |
45 | P2P Exchange With Very High ML Risk | 90 | P2P exchange operator with a very high risk of money laundering |
46 | Payment Processor | 50 | Cryptocurrency payment processor |
47 | Ransom | 100 | Malware ransom |
48 | Malware (excluding Ransom) | 100 | Developers and sellers of malware (hidden miners, installers, etc.) |
49 | DDOS service | 100 | Providing DDOS services |
50 | Phishing service | 100 | Phishing service |
51 | Scam | 100 | Recipients of funds as a result of fraud (including pyramid schemes) |
52 | Stolen Coins | 100 | Stolen cryptocurrency assets |
53 | Scam ICO | 100 | Fake ICO |
54 | OTC Exchange With High ML Risk | 75 | Cryptocurrency exchange with a high risk of money laundering |
55 | OTC Exchange With Low ML Risk | 25 | Cryptocurrency exchange with low risk of money laundering |
56 | OTC Exchange With Moderate ML Risk | 50 | Cryptocurrency exchange with medium risk of money laundering |
57 | OTC Exchange With Very High ML Risk | 90 | Cryptocurrency exchange with a very high risk of money laundering |
58 | High Risk country | 90 | FATF gray list and services of these countries |
59 | Sanction country | 100 | Iran, North Korea, Crimea, Venezuela, Cuba and services from these countries or territories |
60 | Terrorism | 100 | Participants and sponsors of terrorist activities |
61 | ATM | 50 | Cryptocurrency ATM |
62 | Paramilitary organization | 100 | Official accounts of paramilitary organizations and their participants |
63 | State bodies | 75 | State bodies |
64 | Cracked software | 100 | Cracked software |
65 | Media | 25 | Media |
66 | Other | 50 | Other |
67 | Seized funds | 0 | Seized funds |
68 | Whale | 10 | Whale |
69 | Green Point | 50 | Green Point |
70 | Red Point | 100 | Red Point |
71 | Unnamed service | 75 | Unnamed service |
72 | Sanctions | 100 | Sanctions |
73 | Religious associations | 50 | An organization formed for the purpose of joint confession and dissemination of religious teaching |
Сriteria:
Сriteria | Significance of risk |
---|---|
High | 100 - 75 |
Medium | 74 - 26 |
Low | 25 - 0 |