E-commerce Fraud Facts
blog.siftscience.com
E-commerce Fraud Facts
1–10 of 46 posts
Re: E-commerce Fraud Facts
#2The patterns that emerge for fraudulent orders are amazing. And, as the article notes, often specific to a particular merchant. Fraudulent orders often come in waves lasting up to several months, and pattern recognition can be particularly helpful in identifying parts of those longer waves.
I'm also working on a project in this space - http://www.merchantprotector.net
Re: E-commerce Fraud Facts
#3We've found that good indicators include: a large distance between billing and shipping addresses, a large distance between estimated IP location and billing address, large order size, using a free email like gmail/yahoo/hotmail (that's the smallest of the factors, but virtually all of our fraud orders use them). Even combining these and others with a threshold, it's still hard to reliably detect without too many false positives.
Re: E-commerce Fraud Facts
#4Fraud "facts" like that applied in a blanket fashion would frequently flag international customers, 3 of the 5 rules listed apply to me.
Re: E-commerce Fraud Facts
#5This is awesome stuff. Theoretically. But Sift doesn't actually make these functional/actionable right away through their service (even though they could). We signed up, love (LOVE!) the idea, but they keep asking for more data before returning meaningful results.
Their home page says, "Get going in minutes: Integrate in just three steps: paste a Javascript snippet onto your site, log transactions from your servers to our REST API, and send examples of banned users." But it isn't so. They require a long-term in-depth model of your site/usage including multiple fraudulent examples (what if you've mostly solved fraud?) before returning meaningful results.
According to this post and previous posts, they should be able to return meaningful results with very basic things: time of transaction, email address, etc. They should start off with: "Here's our recommendation, but it's based on limited information so we feel X strongly about it." Instead they say: "Give us more information, we can't help you yet."
Re: E-commerce Fraud Facts
#6Are you familiar with ensemble methods and boosting algorithms?
How does Sift Science combine multiple signals like these (which individually are pretty weak) into one fraud detection system with a high level of predictive accuracy?
Re: E-commerce Fraud Facts
#7With domain name registration the factors that we have noticed that are almost certainly fraud orders are (in various combinations):
1) credit card payment is all lower case and/or obvious non understanding of how US addresses are formatted
2) domain name has "hack" or some foreign sounding word. Or is anything related to vietnam (get plenty from vietnam)
3) IP location doesn't match customers location
4) Multiple attempts in a row with different credit cards
5) Registrant name doesn't match the name on the credit card and/or address
6) Customer name doesn't relate to email address used in any way.
Once again no one factor is definitive usually but a combination of several together almost always indicate a fraud order.
Those are off the top there are more. Bottom line is when you simply look visually at the orders you can tell with near 100% certainty that an order is fraudulent.
Otoh, here is a fictional example of an order that wouldn't appear fraudulent at all:
domain: bobspartycity.com
Registrant: Bob Wagner Address: 76 Walnut St., Williamette IL bobspartycity@gmail.com And IP is in that vicinity etc.
...etc. It could be of course but we've never had a case where a fraudster puts much effort into faking an order using knowledge of what we look for.
Re: E-commerce Fraud Facts
#8Fraud "facts" like that applied in a blanket fashion would frequently flag international customers, 3 of the 5 rules listed apply to me.
A US company selling internationally will have to be very careful, especially at first, applying any "fraud best practices."
One of the (apparent) advantage to the OP's service is that there is a built-in learning component, presumably tailored to your particular store. That should help quite a lot with recognizing patterns unique to an individual situation.
Re: E-commerce Fraud Facts
#9This is awesome stuff. Theoretically. But Sift doesn't actually make these functional/actionable right away through their service (even though they could). We signed up, love (LOVE!) the idea, but they keep asking for more data before returning meaningful results. Their home page says, "Get going in minutes: Integrate in just three steps: paste a Javascript snippet onto your site, log transactions from your servers t…
Yeah we work with one of their competitors and it is not simple. It takes an investment and thoughtful analysis to get use out of these services. No surprise. It's an inexact science.
But, they really shouldn't advertise it as easy to use.
Re: E-commerce Fraud Facts
#10This doesn't seem really smart since some of those apply to me. I am not from America and I sometimes order from there meaning it would seem like I'm ordering at 4am when its 2pm my time. Another thing is the email, I know a lot of people with their birth year inside their email. What about them?