As any online retailer knows, e-commerce fraud comes in many forms. Although there’s no one-size-fits-all approach to fighting the various types of fraud, knowledge is your best defense against hackers and fraudsters. Remaining vigilant helps you stay ahead of the game and prevent fraud before it happens.
If you’re already using Avolin Protect, you might have configured it to approve or cancel orders automatically through our fraud prevention workflows. But when certain orders require human oversight, you can review additional data points on the Order Review page to help guide manual decision making.
Keep reading to learn our first tip about what data to look for and how to interpret it.
Payment and Order Data
One of the key things to examine when reviewing an order is information about the order itself, including its price, time placed, and payment data. While these factors alone may not necessarily guarantee the presence of fraud, they can be a strong indicator of suspicious activity.
One of the most prominent indicators of fraud is an order’s price. Although an expensive order may be exciting at first, be careful that it isn’t too good to be true. Large orders are not inherently suspicious, especially if your store tends to attract big spenders, but a sudden influx of unusually high-value orders may be cause for concern. Fraudsters may place large orders with the intention of later filing a chargeback or committing credit card fraud using stolen credentials. When you review an order’s price, take note of the specific items that a customer ordered, especially if your products have a high resale value. A customer who purchases 10 expensive cell phones may be planning to resell those devices to a third party.
While not as obvious as price, the time that a user placed an order can also shed light on its risk of fraud. E-commerce lends itself well to late-night shopping, so orders placed at 2:43 a.m. may just mean that your customer base includes a night owl or two. But when examined in combination with other factors, including price and location, orders placed at odd times may increase the chances of fraud. For example, if you receive a spike in orders during a time when most of your customers are asleep, you may be receiving invalid traffic from bots who place orders as part of a retargeting fraud scheme. A single order placed in the middle of the night may not warrant suspicion, but if you receive a dozen expensive orders between 4 a.m. and 4:10 a.m., they may be a sign of fraud.
Payment data can be a telltale indicator of fraud. One of these risk factors is the number of payment attempts for an individual order. It’s not unusual for customers to accidentally mistype their credit card number while placing an order, but a customer who attempts to enter five or six different sets of payment info is highly suspicious and likely using stolen credit card data.
Look out for part two – all about customer and location data – coming very soon.