Are false declines costing you more than actual fraud?

False declines are legitimate debit and credit card transactions that are declined due to overly sensitive fraud detection methods and algorithms. This can be a big deal for merchants and banks alike.

A recent JP Morgan Payments and Data Intelligence article reported that, while actual fraud loses represent an estimated 7% of the total cost of fraud, false declines losses amount to 19%.

A blog from May 2025 (Worldpay for Platforms) reports that, in the US, 11% of online transactions are falsely declined.

Fraud filters are designed to protect merchants and issuers, yet they are increasingly blocking genuine customer transactions at the checkout. 

The problem

False declines are legitimate transactions incorrectly rejected by fraud filters, costing merchants more in lost revenue and goodwill than actual fraud losses. This occurs when overly aggressive fraud prevention systems block genuine customers at checkout, leading to lost sales and negative customer experiences.

As many as four in ten consumers say that, if a merchant falsely rejects one of their purchases, they will refuse to shop with that merchant again in the future.

The impact

The negative effects of false declines include:

• Lost revenue

The most direct impact of false declines is the lost revenue associated with the declined purchase

• Lost customers

Legitimate customers do not enjoy being rejected without a valid reason. Declined purchases may encourage them to shop elsewhere. At worst, they may decide to never shop with you again

• Lost reputation

There needs to be a reasonable balance between fraud prevention systems and a frictionless customer experience. If the customer experience is impacted in any way, you may gain an unwanted reputation for payment hassles with consumers

The causes

We see four major issues causing false declines in credit and debit card transactions:

1. Overly aggressive fraud detection rules
   Static rules or badly targeted fraud models can flag legitimate transactions as suspicious.
2. Insufficient or incorrect customer data
   Missing address data, outdated phone/email addresses or incomplete cardholder profiles trigger verification failures.
3. Bank or network authorization friction
   Issuers and card networks may auto-decline transactions that appear unusual with suspicious parameters such as new merchants, location changes and large purchase amounts.
4. Technical or connectivity failures in payment routing
   Payment gateway outages, acquirer routing issues and peaks in processing capacity can cause legitimate transactions to fail.
   
   

The resolution

Today’s intelligent and automated fraud detection methods demand a balanced strategy with closer human oversight. Otherwise, the negative financial impacts of false declines could outweigh the positive benefits of actual fraud reduction.

How can these issues be resolved and the worst impacts mitigated?

1. Overly aggressive fraud detection rules

• Use adaptive, machine-learning-based fraud scoring
• Incorporate behavioural and device context to reduce false declines
• Continuously recalibrate fraud thresholds

2. Insufficient or incorrect customer data

• Strengthen customer data capture and verification at onboarding
• Use real-time data enrichment and account updater services
• Implement secure customer identity linking across channels

3. Bank or network authorization friction

• Implement real-time customer authentication (e.g., biometric confirmation via banking apps)

• Use network tokenization and 3-D Secure 2.0 to provide additional verification responses
• Improve issuer–merchant data sharing about transaction context

4. Technical or connectivity failures in payment routing

• Use smart payment routing across multiple acquirers
• Implement automatic re-routing paths when a gateway is down
• Monitor payment success rates in real time and adjust routing dynamically

Is AI the cure for false declines?

False declines are a massive problem. The good news is that AI has not only improved true fraud detection, but it is now being employed to reduce false declines.

New advancements in AI are going to play a key role here. For example, machine learning can determine the best schedule for resubmitting a ‘failed’ 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 are a subscription merchant.

Of course, deploying any AI-enabled solutions depends on your payment provider and other third-party vendors.

So, are you suffering from an unacceptable level of false declines and concerned about how to fix it?