How to Prevent Cryptocurrency Account Takeover

by Tamas Kadar
In the online world, having more customers is always better.
Or is it? Here’s our guide on spotting high-risk users before they can damage your business in the long run.
High-risk customers are those who could potentially turn into a threat to your company. In the online world, that threat is often related to cybersecurity, fraud, or compliance issues.
Because risk takes on many forms depending on your industry, a risky user could be one who is likely to:
The key, of course, is for companies to have a clear idea of where risk lies within their vertical, for instance by going through a fraud risk assessment checklist or other RiskOps strategies.
Spotting high-risk users is paramount to operating a safe, successful online business. It may even be considered a form of customer segmentation.
Here are four key reasons to identify them:
Ensuring your business covers all these areas will ultimately help you boost revenue, operate safely, and remove potential threats that could harm your growth.
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Let’s look at some common types of high-risk customers you might face, when to look out for them, and how to prevent them from harming your business.
First on our list are users who are going to purchase a product or service from you using stolen credit card credentials.
This is bad news for a number of reasons, especially when the original cardholder requests a chargeback:
The last point can be particularly damaging. Card operators (Visa, Mastercard, AmEx) might consider your business high-risk, which would increase your card processing fee. In fact, they even ban from their network businesses that incur too many chargebacks.
For most businesses, fraudsters with stolen credit cards are most obvious at the checkout stage.
You need to have information about the user, and more importantly, their payment method. However, for companies that require a credit card to sign up, this is also a good touchpoint to check the payment details.
To spot a suspicious payment, you need to compare the card data with the user data. This will help to highlight suspicious discrepancies, such as:
A lot of the heavy lifting can be done thanks to a BIN lookup. This will help you extract valuable information from the first few digits of the card number alone. You can try out SEON’s card BIN lookup below:
Free BIN lookup!
Enter the first 6 or 8 digits of a card number (BIN/IIN)
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A specific case in the world of finance, money laundering is a punishable crime for the perpetrator, but may also result in hefty compliance fines for the institution that enabled it. AML processes are standardized to help spot high-risk users who may be attempting to conduct such financial crimes.
This would be at the registration stage, and later at the transaction stage, where you can enable transaction monitoring.
Here is an example of using software to identify risky users, setting up controls to flag people operating in sanctioned countries. In this case, SEON’s engine will flag all users from high-risk countries as defined by the operator.
In the example above, we are creating a list that includes Pakistan, Burkina Faso, and Yemen, to ensure we meet regulations regarding customers in these countries.
Then, if an IP address points to one of these countries, we can consider this user high-risk for our business. Of course, this does not necessarily mean they are a fraudster or criminal.
However, scrutinizing their actions and assessing whether there are additional red flags can make all the difference in stopping this type of financial crime.
Multi-accounting users – fraudsters who sign up for a service multiple times under different names – are a huge problem in several industries.
They mess with your analytics. They abuse your marketing promos via referral fraud. Plus, they tend to not be trustworthy individuals, who could be working for sophisticated fraud rings.
This is why identifying them as high-risk users as soon as possible is paramount to operating safely online.
This would be at the registration stage, when you can get access to data such as their IP address, email address, and phone number. More importantly, the device they use to connect to your site can highlight similarities between ostensibly separate accounts.
Here again, collecting as much data as possible will take you a long way down the road towards security. What’s particularly important is to pay attention to the device data, including:
High-risk users who sign up multiple times will attempt to make it look like they are doing so from different configurations of software and hardware. But you can still pick up on suspicious data, as shown in the demo below:
How you define a high-risk user might depend on your industry and business model, but the idea is always the same: Fail to flag them early, and there could be a negative impact on your entire company.
In the online world, identifying high-risk customers always starts with better data. This goes beyond the identifying info they present themselves – name, address, credit card – but also alternative data you can gather via data enrichment.
That data can then let you automate your risk management and customer segmentation, allowing for safe, compliant operations, and empowering growth.
Partner with SEON to catch high-risk users with real time data enrichment, customizable rulesets and advanced APIs.
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Who is considered a risky user varies from one business to the next. But identifying them always starts by gathering data. Adding a wealth of data points that are suited for your business model via real-time data enrichment helps you get a full picture, which can inform the next steps.
User risk is a metric you can rely on to gauge the likelihood that a user will be beneficial or dangerous to your business. In fraud prevention, for instance, this is expressed via a risk score, also known as a fraud score.
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Tamás Kádár is the Chief Executive Officer and co-founder of SEON. His mission to create a fraud-free world began after he founded the CEE’s first crypto exchange in 2017 and found it under constant attack. The solution he built now reduces fraud for 5,000+ companies worldwide, including global leaders such as KLM, Avis, and Patreon. In his spare time, he’s devouring data visualizations and injuring himself while doing basic DIY around his London pad.
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