Selling digital and virtual goods is a lucrative business, but one that also attracts a lot of fraud attempts. The logic is obvious: no shipping requires no physical presence or appearance of one, fast delivery allows fraudsters to quickly buy multiple items and exploit much more of every stolen card, recourse by the seller is almost impossible due to the speed and finally, reselling stolen products is much easier than tangible goods. After our blog was featured in Balanced’s post about fraud, we saw multiple questions about fraud in digital goods. One of them was this comment on HN. One reason for Signifyd getting a lot of retailer attention is our ability to provide quality fraud prevention decisions that help reduce fraud in cases where there’s little recourse. We wanted to share some insights.
Common wisdom about preventing fraud in digital goods is abound. We’re not looking to repeat the regular tips – using IP address to billing address distance, purchase velocity, email domain type and device fingerprinting as indicators. What we’d like to do is add some more details as to why these things often fail, and suggest a few best practices. Here are some:
- Digital goods purchases provide a quick feedback loop, allowing fraudsters to test and learn fast and adapt. Deploying rules with a single threshold or indicator (e.g. number of past purchases over 4, or IP country must match BIN country) and rejecting 100% of purchases immediately simply provides faster feedback. Either compose rules that have multiple indicators, randomly reject less than 100% of purchases, or implement a random delay in your response.
- IP to billing address location is a complex indicator. Simply measuring distance won’t work when the network is mobile, and setting a single threshold won’t work in most countries. Use sources like GeoIPOrg to understand what connection this IP comes from, and implement bins to your distance function.
- Email domain type is relevant but simplistic. After you weed out the free but rare ones (bad) and corporate emails (usually good) you remin with a ton of Gmails. What then? Using online searches to determine that this email is actually tied to a person is an important next step.
- Customer browsing patterns are highly indicative. New customers, returning customers and fraudsters all navigate differently on your website. Count the number of clicks to initiating a purchase, as well as which types of pages new customers pass through. You’ll see obvious patterns emerging.
- Don’t wait for chargebacks to come. Have one person on staff reviewing purchases randomly to detect emerging trends and respond to them.
- Don’t use 3DS. You will pay much more in lost business than prevent fraud.
Fraud in digital goods is a real problem, but a solvable one. Don’t let the threat of lost money shut down your business and drive you to blocking whole countries from your system. And, give us a buzz. We’d love to see how we can help you.