False declines happen when a legitimate order is blocked or questioned as if it were fraud, and they cost merchants far more than actual fraud does. Online stores can lower their false decline rate without weakening protection by manually reviewing high-risk orders, understanding why declines happen, avoiding rejections based on assumptions, verifying purchases with customers, and combining skilled analysts with machine learning. This multilayered approach pairs human judgment with automated screening to approve more good orders.
For most of us, when we want to buy a product, we want to buy it NOW. But what happens when you can’t, because your order gets declined?
The news gets worse: false declines happen more often that most people think. In fact, about 40% of Americans have had a purchase transaction falsely blocked or questioned, even though the order was legitimate, the card number was valid, and the transaction should have been processed.
Recent security breaches and increased sophistication among fraudsters have made credit card companies more assertive in their fraud prevention efforts. As a result, they’re expanding their fraud criteria, hoping to capture more deceitful transactions.
As consumers and retailers alike increasingly suffer the negative effects of false declines (also known as false positives – as in, falsely delivering a positive fraud score or fraud verdict), the dollar value of lost purchases is also skyrocketing. The impact these false declines can have for e-commerce merchants is concerning.
What causes a false decline, anyway? It can be tough to decipher. Credit card companies and payment gateways don’t openly publish their sophisticated algorithms for flagging transactions, so they can prevent fraudsters from circumnavigating the process.
But we know this: Most fraud protection systems use a complex formula (weighing up to 500 distinct elements) to comprehensively assess the risk that a transaction might be fraudulent.
With this level of complexity, there’s greater room for error.
Recent statistics report that:
Clearly, false declines present a massive challenge for merchants, payment processors and banks. How can a business prevent fraudulent transactions without alienating the customers who are caught in the middle?
We’ve got some suggestions for you.
Here are five ways to lower your false decline rate while still maintaining appropriate levels of protection for your business.
The problem is that nearly 30 percent of all online orders will be subject to review. As a result, this option can be time-consuming and a potentially significant operational expense, making it less practical for merchants with high order volumes. If you don’t have the in-house expertise to manage these reviews, it might not even be an option for your business.
Understanding the context in which a purchase was made will always benefit your bottom line, your decline rate and your customer relationships.
As real-life human beings, consumers don’t all fit neatly into one-size-fits-all packages. We travel internationally, make large and impromptu purchases, and ship gifts to friends and family in far-flung corners of the world.
This reality makes it incredibly tough to tell the difference between fraud and legitimate orders, and merchants will always be at risk of falsely turning away actual purchases.
Avoid this risk by leveraging an industry-leading combination of machine learning and human intelligence to spot fraud and validate legitimate purchases. As a result, you’ll keep (and build) your client base without losing sales to emerging fraud behaviors and friendly fraud schemes.
Maximize security. Minimize credit card false decline rates. Improve customer retention. What’s not to like?
To learn more about reducing false declines as part of a robust fraud-prevention program, contact us at (855) 379-4611 or contact@clear.sale.
A false decline, also called a false positive, happens when a legitimate transaction is blocked or questioned as if it were fraud, even though the order is valid and the card is genuine. The customer is real and the purchase should have been processed, but the fraud system flags it anyway.
Security breaches and more sophisticated fraudsters have pushed credit card companies to broaden their fraud criteria to catch more deceptive transactions. Fraud systems can weigh up to 500 distinct elements per transaction, and that level of complexity leaves more room for error, which catches legitimate orders in the net.
False declines cost merchants far more than fraud itself. The article reports retailers lose more revenue to false declines than to genuine credit card fraud, and a meaningful share of customers who experience a false decline will not return to that merchant, compounding the loss.
The article recommends five steps: manually review questionable and high-risk orders, understand why declines occur and tune your settings, avoid rejecting orders based on assumptions, contact customers to verify a purchase, and use multilayered protection that combines analysts with deep-learning algorithms.
Rejecting orders on generalizations, such as assuming every cross-border purchase is fraudulent, costs merchants legitimate sales. Decisions made without detailed data lose revenue, and every good sale you accept gives you more information to fine-tune your fraud process.
Manual review helps identify truly fraudulent orders, but nearly 30 percent of online orders can be subject to review, making it time-consuming and costly. For merchants with high order volumes or without in-house expertise, full manual review may not be feasible, which is why a combined human-and-machine approach works better.