The SEON Online Fraud Dictionary – 2021 Edition

The SEON Online Fraud Dictionary – 2021 Edition

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The SEON Online Fraud Dictionary – 2021 Edition

Download our fraud dictionary to stay on top of the latest industry terms.

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At SEON, we keep a
close watch on the latest fraud trends. That’s why we spend as much time interviewing fraudsters as we do talking with fraud managers. And our goal is always to share our findings with our audience so that you may make more informed decisions. This is precisely the goal behind this fraud dictionary. The vocabulary of online security and fraud prevention evolves fast, and it’s important to keep up with the latest terms. But it’s also important to know the basics if this is your first entry into the world of cybercrime. 

We’ve compiled a list of fraud terms and fraud definitions in this downloadable guide.

Some Examples of Fraud Detection Terms

As you will see, our fraud dictionary covers fraud terms that every fraud manager and RiskOps, or risk operations team member should know, and which should be useful to elevate your fraud prevention tactics:

2FA

Stands for two-factor authentication. When a user wants to access a website or app, they need to provide a single piece of authentication (SFA) in the form of a password. Adding another method is called two-factor authentication, and it improves security. You will also hear the name multi-factor authentication.

Authentication factors can include facial scans, ID cards, SMS confirmations, security tokens, or biometric fingerprints, amongst others. According to Google, 2FA helps reduce 66% of targeted attacks, and 99% of bulk phishing attacks.

3D Secure

A security protocol designed for online credit and debit card transactions. It is designed as an additional password validated by the issuer, which helps transfer liability to the customer in case of fraud. 3D refers to three domains where the information is checked: issuer domain (where the money is taken from), acquirer domain (where the money is going to), and interoperability domain (the whole payment infrastructure, including software, merchant plugin, card scheme, servers, etc…). The newest version of the protocol, 3D Secure 2.0, adds more data points for devices and IP. 

Canvas Fingerprinting

A form of online tracking. It uses the HTML5 canvas element on web pages to identify and track browser, operating system, and installed graphics hardware. It is used in device fingerprinting.

Examples of Fraud Techniques Terms

Next on our list, you will find dozens of definitions relating to fraud techniques in a variety of industries, such as iGaming or eCommerce.

Burner Phone

Also called a “burn phone”. The term originates from the drug-dealing world and is used for inexpensive mobile phones designed for temporary use. It allows fraudsters and criminals to link an account to a disposable phone number, for instance, to bypass 2FA.

These days, phone numbers can be generated via burner phone apps or services. These work like prepaid phone cards, only allowing you to use them for a limited amount of time before being recirculated. Because they go through your phone’s original cellular data, they are not untraceable.

Matched Betting

In iGaming, this refers to using multiple accounts on gambling sites to improve betting odds and make money from free offers. A person will place a Back bet (backing a certain outcome). They will then create another account to place a Lay bet (backing the opposite outcome). This cancels out the losses but allows them to profit from the free bet offer. Note that matched betting is legal in some regions, such as the UK.

Money Mules

People who receive money into their account and transfer it elsewhere for a fee. It is usually done for money laundering, which makes money mules complicit in illegal crimes.

Like with address drop scams, money mules are often unaware they are helping criminals. They are commonly found via fake job posts, and hired under false pretences, for instance forwarding money a charity in a foreign country.

Examples of Cyber Security Terms You Should Know

Fraud, cybersecurity and cybercrime often overlap. This is why it’s useful to know about the terms you might encounter when attempting to protect your business.

Heuristic rules in computer science help solve a problem faster and with fewer resources than with classic detection methods. In fraud prevention, it can be a system that blocks transactions quickly based on a blacklisted data point such as user ID, email, browser hash or other. 

It’s worth noting that heuristic rules use algorithms that trade accuracy for speed. This makes them particularly useful for time-sensitive requests, for instance when trying to decide if a transaction is fraudulent or not as quickly as possible.

Honeypot

A tool that cybersecurity experts use to lure criminals and fraudsters. It is a system deliberately used to be exploited so that the security team can see and learn how attackers operate.

Ransomware

Malware that blackmails the user in order to be removed. It is a virus that blocks access to a computer via encryption unless a certain sum is paid (via cryptocurrencies to enjoy anonymity). The criminals usually threaten to delete important files or disable the entire computer if the money isn’t paid by a certain deadline.

Examples of Technical Terms that Are Useful for Online Businesses

Last but not least, our fraud definitions cover some important technical terms that anyone doing business online should know. 

HTTPS

Hypertext Transfer Protocol Secure. The SSL-secured version of HTTP, which adds a security layer for connections between browsers and websites.

SSL / TLS

Secure Sockets Layer, and Transport Layer Security. Certificates that confirm encryption between a server (typically a website) and client (browser). The secured connections are established with a “Handshake” protocol, which can be analysed by certain tools.

Unsupervised Machine Learning

The goal of unsupervised machine learning is to make sense of data that has not yet been labelled, that is to say, where we do not have the right answer. It uses different algorithms to identify anomalies, irregularities and outliers compared with previous historic data.

One method is to automatically flag data points that noticeably deviate from the statistical norm. Through training, the machine learning system can then become more efficient at identifying regular noise from abnormal behaviour. This is helpful to identify things like seasonal changes without increasing false positives.

Do You Need a Fraud Dictionary?

As the full cost of online fraud continues to balloon, it’s more important than ever to stay in the know. Download our fraud dictionary today for free, and get a headstart on the fraud terms and definitions that will help you make better decisions and protect your company.

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