False declines are legitimate transaction attempts that are declined because of suspected fraud. They are the so-called ''false positives'', fully valid transactions classified as invalid, and rejected by the Access Control Server (ACS).
Picture this - you're about to purchase something online, let's say a new smartwatch. You spend some time researching all of the functionalities. You find the best deals offered to you from various merchants. That's it! After hours, maybe even days of researching online, you are finally ready to make a purchase. You enter all of the required details necessary to finalize your purchase, and – your order declines. Frustrating would be an understatement for this situation.
Now let's examine the next steps. The cardholder will most likely turn to the competition or use a different credit card in order to process their order successfully. Either way, there will be a loser at the end of this story. The cardholder will keep this unpleasant situation in their mind. The chances of them using the same declined credit card or returning to the ''problematic'' merchant are slim to none.
And there it is; a missed sale, reduced revenue, and an unhappy customer - the three horsemen of false declines.
The occurrence of false declines is closely connected to the anti-fraud solution used by the merchant, issuer, or acquirer. The cardholder is usually presented with a generic message such as ''transaction refused''. This offers no additional information that explains the decline or guides the cardholder to take the next step.
Common reasons for false declines involve the following:
Also, anti-fraud solutions based on behavioral analysis might classify a transaction as fraudulent, while it is, in fact, a valid one. Let's say that the cardholder has a pattern of purchasing low-value items online, not more than 10 EUR per transaction. All of a sudden, that same cardholder decides to book an all-inclusive trip online. Regardless of sufficient funds and correct card information during checkout, the transaction might be blocked because the pattern is unusual, and the system flags it as suspicious or fraudulent activity.
There is a fine line when it comes to configuring the ACS solution in order to identify suspicious transactions correctly. False negatives represent transactions that are fraudulent but are valid according to the system. On the other hand, we have false positives, which represent valid, honest transactions that end up as false ones. Configure the system ''too loosely'', you're going to end up with false negatives. Set it up ''too strictly'', you are risking a high number of false positives, i.e., false declines.
If we examine the end impact of fraudulent transactions, we need to keep in mind that the loss is not equal to the amount of the processed fraudulent transaction. It can be anywhere from 100% (gold) to 0% (digital goods) of the amount displayed in the web store. If we take sneakers as an example, the total cost of loss will be equal to the manufacturing cost. It is usually as low as 5% of the displayed price.
When talking about false declines, the end impact is much more significant. After receiving a notification about an invalid transaction, the cardholder doesn't have any guidance on the next steps. They will most likely use a different credit card or look for the same product/service in the neighbor's yard, the competition. Either way, they are leaving with an unpleasant experience with the overall service, and it is not likely that they will use the same rejected credit card or revisit the same merchant.
Riskified surveyed 5000 US-based consumers in order to find out more about their online shopping experiences and fraud. Regarding our topic, the survey discovers that almost one third of shoppers in every segment are wrongfully rejected during a purchase, resulting in a false decline. After being rejected, 42% of shoppers abandon their cart immediately and move on to the next best thing. If we look at the big picture, that means that all acquisition costs and efforts went through the window because of a ''single'' false decline.
False positives are extremely expensive. The Global Fraud Survey published by the Merchant Risk Council states that the average online store rejects 2.6% of all transactions under the claim they might be fraudulent. The pricing pattern says that the higher the price, the higher the percentage of declines (e.g., merchants decline around 3.1% of orders over 100$).
3D Secure 2 enables issuers to access ten times more transaction data than before, which results in more precise risk analysis and profile creation of the cardholder. The end result? Less false declines, among other benefits, of course. Both merchants and issuers are able to increase profits and keep their customers satisfied and returning to use their service.