Cryptocurrency addresses with a high Risk Score

How not to get hooked by scammers
Cryptocurrencies have become an integral part of many Internet users. Many still consider cryptocurrencies to be used only for investments, but not everything is so simple.
Cryptocurrency has become a reliable way to transfer funds, in particular, abroad or to pay for services that are not available under sanctions. However, the use of cryptocurrencies is always associated with risks. We are talking about various fraudulent activities, thefts and other security issues. Where there is more community, there is more scam, right?
When you are given a loan, your rating is checked through the service of the Bureau of Credit Conditions. When you order a taxi, the taxi service checks your rating to provide you with the rating of the appropriate taxi driver. When you enter the university, your rating is checked by the results of the Prom Examination.

It so happened that any rating is a reflection of the behavior of the address, a prediction of how a person, service or something else will behave. By analogy, the concept of Risk-Score appeared in cryptocurrency.
In this article, we will look at what the Risk Score is, how it works, what risk assessment criteria can be taken into account, how high and low Risk Score addresses are analyzed, and how using the Risk Score can help protect cryptocurrency assets.
Risk Score Calculation Methods
ConsenSys is a start-up blockchain created by Ethereum network co-founder Joseph Lubbin
Risk Score is a cumulative indicator in the range from 0 to 100 in terms of risk factors.
There are several foreign analytical companies that provide risk scoring services for cryptocurrency addresses. Each company has different scoring methodologies and weightings, but in general, the risk score is calculated based on:
  • Transaction history
    Frequency, volume and type of transactions that pass through the address
  • Connection with known scam addresses
    Analysis of the links of the address with other addresses where fraudulent activities were previously registered
  • Relationship with the theft of cryptocurrencies
    Analysis of the links of the address with the addresses where the theft of cryptocurrencies was registered
  • Level of anonymity
    Analysis of the level of anonymity of the address and its use in the "dark" networks
  • Usage in authorized countries
    Analysis of where transactions take place and in which countries the addressees and senders of funds are located
And now, on the last point, it is worth dwelling in more detail. Our foreign competitors assure that the address belonging to the sanctioned countries raises the risk-score up to 100. We consider this methodology to be incorrect for many reasons, which we will discuss below. We will not point fingers, but there are competitors who calculate the risk-score only on the basis of regional sanctions.

To solve the problem of an unreliable risk-score, we have formed our own multi-stage methodology for calculating the risk-score. The main features of our solution were the in-depth study of the address, including all counterparties studied in 6 steps. Also, we believe that the funds that appeared in the sanctioned countries have the lowest coefficient when calculating the risk-score.
Deep Risk Score
As we have already found out, the risk-score is calculated based on transaction and counterparty data. So what is a deep risk score?
By this definition, we mean the analysis of all transactions at a distance of up to 6 steps from the address under study. Why exactly 6? Still heard about the theory of 6 handshakes, according to which all people in the world can be familiar. It is this theory that formed the basis for calculating the risk-score. In addition to studying all transactions for 6 circles of counterparties, we also:
we analyze the context in which transactions take place in order to understand what risks may be associated with a particular operation. For example, we can analyze what information is available about the sender and recipient, where transactions take place, what goods or services were purchased, etc.;
analyze data from additional sources, such as blockchains of other cryptocurrencies or databases of fraudulent schemes, in order to get a better idea of the risk associated with a particular address;
we use machine learning algorithms to analyze large amounts of data and identify hidden links between addresses and transactions. This allows us to more accurately determine the risk level of cryptocurrency addresses and warn users about possible fraudulent schemes.
Address: 0x2b...26
Here is an address that was involved in the distribution of phishing tokens. This address at some time became so popular that even official sources like Etherscan gave it the name fake_phishing.

Risk-score is an address of 100 points, which means the highest possible risk for transactions with it. If your address starts exchanging tokens with such an address, you risk increasing your risk score up to the same 100 points.

It is important to note that when analyzing such an address, it becomes impractical to conduct an in-depth analysis, since it is impossible to reduce the risk score by 100 points.

Address: 0x2c…ca
This address belongs to a regular user who actively trades cryptocurrencies, participates in staking, uses DEX, deposits and withdraws money on CEX. However, his risk-score without in-depth analysis is 17 points. This risk-score is within the normal range.

When analyzing this address for several steps, we see that its risk-score is gradually increasing. And in the last step, his risk-score is 75, which is higher than the risk-score for a safe exchange.

When exchanging with such an address, it should be borne in mind that he once contacted not the most decent addresses.
In the future, Risk-Score may become an even more important tool in cryptocurrencies. Users can use this tool to make decisions about which addresses to use for their transactions, as well as reduce the risk of losing funds and cryptocurrency fraud.

There is potential to improve the methods for calculating the Risk Score, including by incorporating new parameters and using more advanced machine learning algorithms. The development of the cryptocurrency industry can also help improve the calculation of the Risk Score, as new technologies and regulatory requirements can lead to more accurate analysis and definition of risks.
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