At Sift Science, we analyze a lot of data. We distill fraud signals in real-time from terabytes of data and more than a billion global events per month. Previously, we discovered that the U.S. has more fraud than Nigeria and solved the mystery of Doral, FL. At our “Cats N’ Hacks” Hackathon last week, I decided to put some of our fraud signals to the test. Working with our Machine Learning Engineer, Keren Gu, we discovered some interesting fraud patterns:
Habit #1: Fraudsters Go Hungry
When we looked at total non-fraudulent (normal) transactions by hour, normal users had slow starts to their mornings. We noticed a slight dip in transaction volume around lunchtime and suspect that’s because people are taking lunch breaks! Happily fed, they resumed activity in the afternoon and activity petered out as users went home for the day.
What about fraudsters?
Fraudsters, however, work through lunch. We don’t see the same dip in activity during lunchtime in the fraudulent sample. It seems that fraudsters are too busy scheming their next move.
Habit #2: Fraudsters Are Night Owls
When we analyzed fraudulent transactions as a percentage of all transactions, 3AM was the most fraudulent hour in the day, and night-time in general was a more dangerous time. This finding is consistent with our historical findings and it makes sense: fraudsters are more likely to execute attacks outside of normal business hours when employees aren’t around to monitor fraud.
Habit #3: Fraudsters Are International
Indian email address domains had one of the highest fraud rates when compared to other top-level domains. However, don’t give up on those great Bollywood movies just yet! We’re only looking at data from the past three months. We’ve seen this list fluctuate quite a bit depending on what new tactics fraudsters use.
Habit #4: Fraudsters Don Multiple Identities
Fraudsters tend to make multiple accounts on their laptop or phone to commit fraud. When multiple accounts are associated with the same device, the higher the likelihood of fraud. The graph above shows how many times more likely a user is fraudulent given the number of accounts associated with the user’s device. Phew, that was a mouthful! Said in another way, a user who has 6 accounts on her laptop is 15 times more likely to be fraudulent than the average person. Users with only 1 account however, are less likely to be fraudulent.
Habit #5: Fraudsters Still Use Microsoft
Some of the most fraudulent email domains are operated by Microsoft. Why could this be? Two possible reasons are that 1) Microsoft has been around for a lot longer and 2) email addresses were easier to create back in the day. Today, websites use challenge responses such as image verification or two-factor authentication to verify your [tooltip tip=”and innocent!”]legitimate[/tooltip] identity.
Habit #6: Fraudsters Are Really Boring
One of the most widely recognized predictors of fraud is the number of digits in an email address. The more numbers, the more likely that it’s fraud. Why? Because fraudsters are boring (and lazy). They use computer programs to sequentially generate email addresses so they don’t have to think of new ones. Emails such as “firstname.lastname@example.org” or “email@example.com” are highly suspicious. However, detecting fraud using email address alone can be really difficult. The only way to really get good at detecting fraud is to look at hundreds of signals, sometimes in the thousands (that’s where machine learning can help).
Habit #7: Fraudsters Are Sneaky
Fraudsters like to create disposable accounts that are short-lived. In analyzing the age of fraudulent user accounts (meaning, the amount of time between account creation and a fraudulent transaction), we found that they sign up on sites and then quickly commit fraud. The longer the account age, the less likely the user is committing fraud. Nonetheless, experienced fraudsters know that fraud detection companies track this type of signal. In the graph above, we noticed “sleeper” fraud agents became active after 30 and 60 days of account creation. Fraudsters are sneaky!
Obviously, the above is not a definitive sample set. Data can help us find potential answers as to why fraudsters behave in the ways that they do, but as statisticians say, “correlation is not causation”! It’s important to use common sense and human intuition when it comes to dealing with fraud.
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Did you like reading about these insights and patterns? What other fraud signals interest you? Let us know in the comments, and we’ll pick out a few to write about in our next installment!
 Data was collected from the past three months over our entire network. From the hundreds of millions of transactions we processed during that time, we analyzed about 6 million. Our “fraud” sample consisted of transactions confirmed fraudulent by our customers; our “normal” sample consisted of transactions confirmed by our customers to be non-fraudulent, as well as a subset of unlabeled transactions. Please keep in mind that every company faces different type of fraud, and that our findings may not be representative of what you see. All transaction timestamps are local to the user.