What Are False Credit Card Declines?
What Are False Credit Card Declines?
False declines are valid credit card transactions that are mistakenly rejected by the bank and are one of the leading reasons consumers are unable to make purchases. Current estimates indicate that about 94% of transactions identified as fraudulent are mischaracterized and should be approved.¹
Of course, there are many valid reasons to decline a transaction. Let’s look at a few different cases.
If you report your credit card as lost or stolen and someone attempts to use it, this would be declined. You’ll sometimes see this referred to as a hard decline. The merchant may have incorrect address or cardholder name information. Or, the merchant may not have passed CVV information (the 3-digit code on the back of your card) when required. Sometimes, if there is a misconfiguration on the payment gateway, you’ll see technical declines.
There are also soft declines; an example of that would be when you have insufficient space on the card.
Typically, there is no problem with trying to recover soft declines, but with hard declines, it is important to understand the nature of the decline before trying to recover it. Aggressive recovery on hard declines can result in negative to catastrophic consequences with your merchant bank.
Who is making the decision to decline the transaction?
If ever you took the time to think about who is deciding to approve or deny your credit card transaction, you might guess the decision lies with the Card Networks (Visa, MasterCard, Discover, AmEx, etc.) or maybe with the bank listed on the card (the issuer). This is a common misconception.
Between the Card Network and the issuer sits another player, the issuer processor, whose role is to provide recommendations on what to do with the transaction. The processor analyzes the transaction and uses a rules-based algorithm to score the transaction before sending it to the issuer.
It’s here that things go off the rails. The issuer processor’s rules-based systems cannot effectively accommodate the multitude of factors involved in the fraud detection process.
Let’s take an example, say a Chase Debit Card transaction occurs between 11 pm and 2 am. Chase knows, based on historical patterns that transactions that occur between these times have a higher propensity to be fraud or chargebacks, so they have set rules to increase the fraud score for these types of transactions. But, in this case, the merchant processes recurring transactions during these times, and most of those transactions are for repeat customers. Chase’s simple rule will have increased the fraud score without considering the merchant’s history with these clients. Chase doesn’t know that you’ve successfully billed these customers multiple times in the past. Even though you’re conducting the transaction at a time frequently used by fraudsters, these are entirely legitimate charges.
To add to the problem, the processor’s customer, the issuing bank, prefers that they err on the side of caution. Issuers are cautious because that if a transaction approves, they make pennies. But, if it turns into a chargeback, they can be on the hook for the cost of the refund or, at the very least, they will have an upset customer who has consumed valuable and expensive customer service resources while handling the chargeback.
With an overwhelmed fraud detection algorithm and a directive to rule out anything that might look like fraud, the issuer processor creates an inflated fraud score to send to the issuer. The issuer evaluates the transaction against some internal rules, including whether there is credit available, and appends the risk score provided by the processor to make a final determination to approve or not. This combination of risk aversion and underpowered fraud detection is the primary source of the false positive problem. Of 13 transactions identified as fraud by the current system, only one will have been, in fact, real fraud.
Learn more about how FlexPay helps clients recover false credit card declines here.
For more information on our approach, read this article on how FlexPay uses machine learning.
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