How Ecommerce Fraud Prevention Rules Impact Approval Rates

As a first line of defense, many businesses rely on fraud prevention rules or fraud filters to protect their bottom line and customers. But rules and filters are black and white, and ecommerce transactions can range from dove grey to charcoal. To protect approval rates, businesses need a comprehensive fraud prevention strategy. 

This guide explains everything businesses need to know about the relationship between fraud filters and approval rates.

Fraud Prevention Rules Can Be Highly Effective

Most fraud red flags make perfect sense on their face: Multiple orders from different credit cards, inconsistent order data and unusual locations, just to name a few. Fraud prevention rules are ideal for these scenarios … or are they?

There could be innocent reasons why a customer could be tripped up by those same rules. Like two members of one household ordering the same thing on two different credit cards, or an aunt on vacation abroad ordering a gift shipped to a niece’s home. 

In those cases, valid orders may get caught up in the fraud prevention net, and your customer satisfaction and approval rate would suffer. 

Fundamentally, fraud prevention rules are just a series of filters. Every ecommerce business owner needs to understand what fraud filters do well and not so well.


Related Reading: The Beginner’s Guide to Fraud Filters


What are fraud filters?

Fraud filters are guidelines established by businesses to block potentially fraudulent orders in their online store. Nearly every modern ecommerce platform includes various fraud filters. Depending on the settings of these fraud filters, they can either alert you to a potentially fraudulent transaction or cancel an order.

The most common types of fraud filters include:

Daily or Hourly Velocity Filter
This filter limits the number of sales that can be processed on your website within a specified timeframe. This helps prevent fraudsters from testing stolen credit card numbers after purchasing lists on the black market. It also helps identify instances where a fraudster makes multiple orders of a product to take advantage of a discount or sale.

Address Verification System (AVS)
This filter checks that billing and shipping addresses are consistent. Fraudsters often use stolen information to buy items and ship them to a location close enough to evade the company’s manual review process.

Card Verification Value (CVV) Filter
This filter checks for mismatches between the CVV number on a card and the one provided during checkout. Be aware that fraudsters are familiar with this filter and can easily include it in the data they hack from data stores with weak security. It’s also common for them to gather this data through account takeover (ATO) fraud, where they gain control over someone’s accounts.

Purchase Amount Filter
This filter monitors for unusually high or low transaction amounts. Companies often predict these based on the average transaction value, setting thresholds accordingly. This can be particularly useful in spotting fraudsters testing stolen payment credentials while trying to avoid detection.

Geolocation Filter
This filter can block orders from specific global regions known for high fraud rates. If certain ZIP codes, provinces, or countries have a notorious fraud history, you can set this filter to reject transactions originating from those areas.

Benefits of using fraud filters

Fraud filters play a valuable role in a comprehensive fraud prevention strategy. They can help flag suspicious orders that fall into the gray area of “could be fraud but may not be.” Those transactions need to be reviewed to determine how they should be handled.

Using fraud filters as a first step in their fraud prevention process helps businesses decrease the number of false declines that threaten their reputation and bottom line and increase their approval rates.

Better yet, companies that work with a fraud prevention provider can either find an easy-to-implement solution (for small businesses) or gain a partner to help them understand the best way to use fraud filters. 

Without this guidance, businesses may experience the downside of using fraud filters in isolation.


Inside Ecommerce: Guide to AVS Mismatch

Be Cautious of Being Too Strict With Fraud Prevention Rules

Many businesses either set their fraud filters too strictly or rely solely on fraud filters. This can result in scenarios that are bad for business.

Fraud prevention rules can inadvertently open the door for fraud

If one fraud filter is good, 100 must be better, right? Actually, layering fraud filters to out-maneuver fraudsters can create chaos. Layered fraud filters can cancel each other out, leaving the company open to rampant fraud.

Filters can also give a business a false sense of security. Fraud prevention is a moving target because fraudsters are always trying new techniques to get around preventive measures. Without the most current fraud trend information, fraud filters may not even detect the fraud that’s happening.

A sweet spot can be found by continually applying new information about fraud trends and schemes to adjust fraud prevention rules.

Poor execution can result in false declines

Concern about the costs of fraud can lead businesses to tighten up fraud filters to the point that perfectly good customers are turned away

These false declines — sometimes called “false positives” — happen when a customer’s valid order is declined because the business mistakes it as fraudulent. There are two types of declines:

  • Hard declines are the result of an error or issue that cannot be resolved immediately. The decline isn’t temporary, and subsequent attempts with the same payment method will likely not be successful. Customers often walk away from false declines angry and embarrassed.
  • Soft declines are due to temporary issues and can be retried. Subsequent transaction attempts with the provided payment method information may process successfully. This is dependent on the customer’s willingness to retry the purchase.

Customer satisfaction and loyalty are the primary casualties of false declines. 

Over the last three years (2020-2023), we’ve surveyed customers to find out their attitudes toward fraud, chargebacks and false declines, and customers made one thing clear: they’ve become less and less tolerant of false declines.  

Our most recent survey found that 41% of customers said they will never shop on a site again after they’ve experienced a false decline, and 32% will take their complaints to social media, potentially creating a negative reputation for the company. 

Even when a store takes measures to correct the situation and persuade the customer to come back, many still walk away: 

  • 11% of online shoppers said they wouldn’t provide clarifying information to a business that had declined their order.  

When it comes to soft declines, where customers give their purchase another shot, we did discover some positive data: 

  • 59% of consumers surveyed said they would at least consider reaching out to customer service to try again after being declined.  

Remember that 41% of customers will never shop with a company after a false decline, a potential blow to a company’s bottom line. Especially when you consider this fact: up to 70% of declined transactions are from legitimate customers. That means over two-thirds of the time, a business may be turning down sales from perfectly good customers. 

A 70% false decline rate is alarming enough. But what raises even more concern are the short- and long-term costs of turning away good orders. 


The Lifetime Impact of Ecommerce False Declines, Generation by Generation


False declines threaten the lifetime value of customers

Think about the customer who won’t re-attempt to make their purchase and will never shop on your site again. That customer represents a lifetime value for your business.  

If they normally spend an average of $100 per month, your company loses $1,200 each year, for every year they would have been shopping with you.  

If that’s a baby boomer or Gen X customer, you could be losing 20-30 years of shopping, multiplied by that $1,200. But if that customer is a millennial or Gen Z shopper? The lifetime value of that customer increases considerably. Now you’re looking at a loss of 40-50 years. That’s considerable revenue that your company will never see.  

In financial terms, it’s safe to assume that every $1 in false declines equals a loss of $13. 

To make matters even worse, if that customer tells their friends and/or posts on social media, your business is now at risk of losing the lifetime value of even more customers.


False declines impact brand reputation

These days, customers who have had their transactions declined for seemingly no reason won’t just suffer in silence; they frequently vent on social media. They post on Google and Yelp to share their product reviews and warn other potential customers about their negative experiences.

It’s a move that can have a lasting negative impact on the businesses they’re complaining about. Thanks to a cognitive bias called the “negativity effect,” consumers tend to perceive negative ratings and feedback as more credible than positive information. The resulting brand damage can be extensive, with online complaints reaching potentially thousands of customers. Shoppers often head straight to the reviews when evaluating a product and a business, and seeing one-star reviews won’t likely incentivize the shopper to complete that purchase.

Fraud Prevention Rules Can Reduce Your Approval Rate

The idea behind the fraud prevention rules used in automated programs is to generate a fraud score as the basis for an approve or decline decision. All orders with a poor score or that trigger a fraud filter will be automatically declined.

Yet, with as many as 70% of declined transactions being legitimate orders, clearly automated fraud programs are declining good orders, costing you revenue and angering your customers.

But many businesses don’t realize this. Assuming these auto-declined transactions are all fraudulent, businesses calculate their order approval rates (or authorization rates) without including auto-declined transactions into their calculations.

This is a mistake. Omitting auto-declined orders from the order approval rate formula will give you an inaccurate picture about what’s really happening with your sales.

Why you must correctly calculate approval rate

To calculate your true approval rate, you’ll need this data:

  • The average total dollar amount of orders placed each month at your store
  • The average dollar amount of orders auto-declined each month by your fraud filters
  • The average dollar amount of orders approved (i.e., became actual revenue) each month

Without an accurate calculation, your business could be losing revenue and customers under the radar. In mid-market and enterprise businesses, low approval rates can go undetected for a long time, compounding the negative impacts on revenue and customer satisfaction. 

The bottom line: fraud prevention rules and filters have an important role to play in protecting your ecommerce business from harm. But they have limitations that can result in false declines that harm your business in other ways. 

A truly effective fraud prevention strategy should be comprehensive.


Everything You Need to Know About False Declines

ClearSale’s Comprehensive Approach to Fraud Prevention 

Not all non-standard transactions are suspicious. Plenty of legitimate orders fall into a gray area. That’s why ClearSale employs a comprehensive approach (in addition to fraud prevention rules and filters) to prioritize order approvals while preventing fraud. 

First, we study fraud as a discipline. Our knowledge of fraud patterns and trends stems from our longest history in the industry and our unmatched experience fighting fraud. We’ve worked with businesses around the world in some of the most high-risk regions and industries to help eliminate fraud threats and prevent false declines, while approving more orders, faster. 

Our massive transaction database is constantly learning as more orders are processed. This makes it easier for us to identify fraud trends as soon as they emerge and use those insights to make more accurate decisions.

Next, we’ve built a multi-layered process to distinguish legitimate orders from fraudulent ones:

Machine learning/AI

We utilize artificial intelligence and machine learning to screen all orders, processing transactions and refining fraud models based on customer behavior. Each order receives a fraud score. Orders that achieve a score within customer-specific thresholds are automatically approved, while those with questionable or suspicious scores are flagged for additional review.

Contextual fraud review

In cases of suspicious and questionable orders, our data scientists and fraud analysts conduct secondary reviews. They apply their expertise and knowledge of current fraud trends, sharing insights with the client’s team to assess the validity of transactions. Furthermore, if requested, our analysts can contact customers directly in a friendly and diplomatic manner to verify purchases, simultaneously training your team in these practices.

Interactive client dashboard

Our interactive dashboard enables clients to review all orders and participate in contextual reviews, including information about VIP clients and orders that may be automatically approved in the future. The dashboard is also used by clients to monitor chargebacks on approved orders, facilitating easier dispute resolution by ClearSale’s comprehensive chargeback management team.

Post-processing audit

Post-processing uses machine learning/AI to validate decisions and identify emerging patterns. Our auditing program, for instance, provides a secure testing environment to analyze randomly selected declined transactions, exploring potential outcomes had the orders been approved. This helps in assessing the accuracy of automated rules set by our clients and allows for their ongoing refinement.

By working with a trusted third-party vendor such as ClearSale, you can better protect your sales, profits and customers from credit card fraud & other types of commerce abuse. To find out more, contact us today.


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