False DeclinesHow to Reduce the Number of Good Orders You Turn Away

Exploring What Causes False Declines, Plus 6 Key Tips to Help You Capture More Sales

Every online order you take is screened before the money moves. Some of that screening is yours, some belongs to your gateway, and some happens at the issuing bank where you never see it.

All of it is tuned to reject orders that look wrong. Some of what it rejects is fraud, and some is a real customer who shipped to their office rather than their home. That customer will not call to tell you; they will buy the same thing somewhere else.

So what is that second group costing you? In this post, we’ll look at what false declines are, what causes them, how to read the message your gateway returns, and how to measure your own false decline rate.

What Are False Declines?

False Decline

[noun]/fôls • də • klīn/

A false decline on a credit card or debit transaction occurs when a legitimate card purchase is rejected by mistake. This may be done by either the merchant or the issuing bank.

At their core, false declines are legitimate transactions that get blocked on the suspicion of fraud, but without any fraud actually occurring. That is to say, you reject a good customer without a legitimate explanation. This tends to happen through a combination of data failures in the authentication process.

Fraud detection technologies may pick up on fraud indicators that either aren’t present, or which suggest something that isn’t happening. There’s no single answer as to why false declines happen, aside from blanket excuses like “software bugs” and “technical issues.” Therefore, many merchants have started accepting false declines as just another “cost of doing business.” This is a mistake, though.

False declines mean more than losing out on a sale. You also upset potential customers, who will get frustrated that they can’t complete their purchase, and definitely won’t appreciate being accused of something they didn’t do.

What Causes False Declines?

Similar to hard and soft declines, false declines have just two possible sources: the bank or the merchant. On some level, the information is misinterpreted by that entity’s fraud detection software, which flags the transaction as potentially fraudulent– even when it isn’t.

A lot of merchants reject legitimate transactions by accident. Part of the problem is that even the most advanced, dynamic fraud filters still rely on algorithmic decisioning based on common fraud indicators. Some of these include:

For their part, financial institutions depend on a multiplicity of fraud detection systems in order to approve or deny transactions as swiftly as possible. Indeed, with the demand for seamless payments ever increasing, it’s easy to see how there might be issues achieving “real-time approvals.”

Some friction points for banks include:

This is not an exhaustive list of indicators. Fraud filters are complex, drawing on many inputs to try and identify fraud. At the end of the day, though, they’re still automated processes.

Fraud detection demands closer human oversight. Otherwise, the financial impacts could be tremendous.

What Does a Fraud Decline Message Mean?

The message tells you which layer stopped the order. It does not tell you why the order looked wrong. A gateway rejection came from rules you or your provider configured, and a processor decline came from the issuing bank. The two sit side by side on an order dashboard, but call for completely different responses. The confusion I run into most often with merchants is that they read the decline text as the reason for the decline. It’s worth being precise about that distinction, because it decides what you are able to do next. Braintree draws the line explicitly in its own documentation: gateway rejections are blocked by your gateway settings, while declines are blocked by the customer’s bank. If your own rule stopped the order, you can change the rule. If the issuer stopped it, you cannot.

What stopped it	Where the setting lives	What you can change	What it does not tell you
Gateway rejection: fraud, risk threshold, address verification (AVS), card code (CVV), or duplicate	Your gateway control panel	The rule, its threshold, and its time window	Whether the order was actually fraudulent
Fraud filter hold or decline, for example Authorize.net’s Advanced Fraud Detection Suite	Your filter configuration	Which filters run, and what action each one takes	Which specific signal tripped, unless the tool reports it separately
Processor or issuer decline	The issuing bank	Nothing directly. You can retry within the network limits, or ask the customer to contact their bank	The bank’s actual reason. Response codes are frequently generic

A filter that holds an order is not the same as a filter that declines it. You can check the difference in your configuration.

The Advanced Fraud Detection Suite from Authorize.net, for example, runs 13 configurable filters, and each can be set to report only, to hold the order for review, or to decline it. Where two filters trigger with different actions, the most conservative action is applied. A merchant who set one filter to hold, but set another to decline based on the same indicators, may see declines they did not expect as a result.

The holding option carries a time limit that is easy to miss. Going back to Authorize.net, a transaction placed in Authorized/Pending Review gives you 30 days to approve or void it, and it expires if nobody acts on the message.

The authorization path

Where an order gets stopped decides what you can change

Every order passes three checkpoints between checkout and approval. The further it gets before a decline, the less of the outcome is in your hands.

Order placed

Stop 1

Your Fraud Rules

✗ Gateway rejection

What you can change All of it

The rule, the threshold, the time window, and whether it declines or holds. Every part is yours to rewrite.

Stop 2

Your Gateway’s Checks

✗ AVS, CVV, or duplicate

What you can change Which checks run

You choose which checks run and what each one does when it fires. The checks themselves belong to the gateway.

Stop 3

The Issuing Bank

✗ Issuer decline

What you can change Nothing directly

The bank decides alone. Retry inside the network limits, or ask the customer to contact their bank.

Approved

The earlier the stop, the more you can change. Stopped at your own rules, you rewrite the rule. Stopped at the bank, you can only retry.

How Do You Measure Your False Decline Rate?

Your false decline rate is the share of the orders you rejected that were not actually fraud. You will never know it exactly, because a rejected good customer rarely tells you. You can get close, though, by tracking the orders you reversed after review and the ones that succeeded on a second attempt.

The question I hear most often from merchants is not “What is the industry average?” Rather, it is “Is my number bad?” There are two numbers worth segmenting here, although merchants routinely run them together.

Your order rejection rate is everything you turned away, and your false decline rate is the portion of that which was wrong. The first is straightforward to pull from your gateway. The second takes deliberate work, though, which is why most merchants know one and not the other. Our guide to the credit card decline rates covers the broader measure. Some published figures to hold yours against:

Measure	Figure	Source
Global average false decline rate	1.51% of eCommerce sales	Datos Insights, formerly Aite-Novarica
Global order rejection rate	5.0% of orders, down from 5.8% the year before	Merchant Risk Council, 2025 Global eCommerce Payments & Fraud Report
Merchants reporting a false positive rate above 10%	14%	MRC 2025
Merchants reporting a rate of 2% or less	22%	MRC 2025
Manually screened orders subsequently declined	Roughly 19%, consistent across every region and merchant size	MRC 2025

None of these figures tells you whether your own rate is too high. A rate of 4% is a serious problem for a merchant selling $20 consumables, but it may be perfectly acceptable for one selling jewelry at $2,000 per item. The criterion is not the industry average. It is whether the revenue you are turning away exceeds the fraud you are preventing, and answering that requires having your own average order value and your own fraud rate in front of you.

Financial Impacts of False Declines

Nobody measures false declines the same way.

The Merchant Risk Council reports them as a share of eCommerce orders. Datos Insights surveys merchants for self-reported estimates. But the widely quoted global figures in the hundreds of billions measure gross transaction value falsely declined, which is not the same thing as merchant loss.

What is more objectively measurable is the shape of the problem. Merchants globally rejected 5% of orders per the recent MRC Global eCommerce Payments and Fraud Report, and 14% of merchants reported false positive rates above 10% of orders.

For merchants, this is not a discussion about a rounding error; it translates to real financial losses. The negative effects of false declines include:

Reducing fraud and chargebacks is the top strategic priority for 42% of the fraud professionals in the MRC survey. Improving the customer experience sits just behind it at 38%. Those two priorities grind against one another at exactly the point where a filter threshold gets set.

Is AI the Cure for False Declines?

False declines are a massive problem. Good news, though: help seems to be on the way. 

New advancements in artificial intelligence (AI) are going to play a key role here. Machine-learning technologies can allow you to introduce dynamic routing, for instance. This means you can route transactions to the bank so as to clarify details and improve the odds of authorization. As one example, you can route a cross-border transaction through a processor located in the same country as the buyer.

AI may also let you re-attempt authorization after a decline, by changing the circumstances (time and date, for example) of the payment submission to improve the odds of authorization. Development circles refer to this as "retry logic."

Machine learning can also uncover the best schedule for resubmitting a transaction. It does this by leveraging data from similar submissions that received authorization to find the best solution.  This can be especially useful if you’re a subscription merchant.

Your success depends on reducing churn. Any progress on that front will help. Of course, deploying any of these AI-enabled solutions depends on your payment provider and other third-party vendors.

You should look to partner with providers who can leverage these tools to streamline billing, structure payment flows, and both improve authorization rates as well as customer sevice.

Top 6 Tips to Prevent False Declines 

Before the ideal solution presents itself, however, there are a few internal steps you can take to manage false declines in your operations in the meantime. 

We recommend that you:

The first step is to develop a clear understanding of fraud indicators, as well as the reasons why false declines may happen. The key is to analyze why some transactions get approved, while others are rejected.

Don’t reject orders based on generic indicators. Deploy smarter, more advanced fraud filters capable of understanding context more clearly.

One or two indicators won’t provide enough data. Multiple, complimentary fraud detection tools are needed to provide adequate insight into each purchase.

Don’t just automatically reject all purchases flagged by a fraud filter. Manually review all questionable transactions to ensure accurate decisioning.

Trouble verifying a transaction that was flagged as suspicious? Try reaching out to the buyer directly to confirm the purchase.

Don’t overlook new advances in AI-enabled technology. Fraud is a fast-changing problem; your response needs to evolve just as quickly.

Of course, all these approaches to eliminating false declines on credit card transactions call for significant, in-depth data analysis. You must have access to a broad range of transaction indicators. The more information gleaned, the better. In many cases, it helps to have data that extends beyond just what one merchant can see.

Balancing False Declines & Chargebacks

Managing this situation means maintaining a delicate balance between excessive false declines and excessive chargebacks.

The problem underlying both is the same shortage of data. Most merchants cannot identify where their chargebacks are actually coming from, which means the fraud rules they write are aimed at a target they have not located. The result is predictable enough: more good orders blocked, and the preventable chargebacks still arriving.

Optimizing that balance on your own is difficult, and for most merchants it’s not even realistic. Manual review is slow and expensive, and there’s no guarantee that your decision will be right. It also does not give you is visibility beyond your own transactions, which is important, as comparing the transaction against a benchmark is what the decision actually turns on.

I have not met a single merchant who solved this problem by tightening filters alone.

So the criterion is not your fraud rate, and it is not your decline rate either. It is whether you can see enough of both to know which one you are trading away for the other. If you cannot, then tuning either number is guesswork, and the right partner is the one that closes that gap rather than the one promising the lowest fraud rate.

Chargebacks911® offers the industry’s only data-driven, fully managed solution for chargebacks. We combine advanced, proprietary machine learning with human expertise, developing customized strategies to manage chargebacks and fraud. If you want to know how many good orders your current setup is turning away, contact us for a no-obligation analysis today.