Fraud Filters: The Merchant’s Guide to Building a Better Fraud Prevention Strategy
Wouldn’t it be great if you could spot fraudsters before they robbed you blind?
Well, we have good news: there are a lot of methods you can deploy to identify and reject fraudulent transactions. But, as practical as fraud filters can be, they are often mistargeted or underutilized. As a result, legitimate transactions get flagged and rejected, while fraudsters get by unnoticed.
This article will discuss what fraud filters are and how they work. We’ll also explore how effective they are, and look at why you may need more protection in the long run.
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What are Fraud Filters?
- Fraud Filter
A fraud filter is any technology designed to analyze transaction data and flag commonly-recognized signs of fraud. These fraud tools are designed to warn users of potentially fraudulent transactions, letting users stop and reject untrustworthy transactions.
[noun]/frôd • fil • tər/
Fraud is an unfortunate reality of the card-not-present space. As a merchant, you want to catch fraudsters in the act, rather than deal with the consequences of a successful attack (revenue loss, consumer disputes, and chargebacks).
Fraud filters help weed out fraudulent transactions before or during checkout. They do this by setting stringent verification criteria for each transaction, and identifying transactions that don’t meet those criteria. In effect, a fraud filter can be any technology designed to analyze transaction data and flag commonly-recognized signs of fraud.
For example, let’s say an individual is trying to pay for an item online. They may be asked to type in several card-identifying details to verify they have said card in hand, or identify themselves through other means. If the customer cannot identify themselves or verify their card details, the transaction may automatically be voided.
Common Fraud Filter Parameters
There is no such thing as a “foolproof” fraud prevention tool. However, you can drastically reduce your exposure by implementing multiple fraud solutions that complement each other.
On that note, there are many methods merchants can use to block potentially fraudulent transactions. You have a wide array of fraud filters to choose from:
What About Fraud Scoring?
Any one of these fraud filters can be a fairly powerful tool on its own to help you identify risky transactions before they are approved. However, to get the most out of them, it’s a good idea to use more than one tool at the same time. These tools should also be backed by fraud scoring.
Fraud scoring is a practice that takes data from multiple different fraud filters. This sophisticated machine learning technology examines each transaction based on dozens of indicators. In effect, it lets you quantify the level of risk involved in a transaction.
Fraud scoring then assigns a simple numeric score representing that transaction's risk level. This allows for very easy “up or down” decision making.
Machine learning can teach itself new information the more it is used. For the most accurate scoring, it’s wise to implement this tool soon and use it as often as possible.
Learn more about fraud scoringHow to Choose the Right Fraud Filter Parameters for Your Business
Selecting the right filters involves aligning your security parameters with your unique product mix, transaction volume, and industry risk profile. You’ll also need to consider hidden costs, compatibility with your platform architecture, and the broader context of each transaction.
Fraud filters shouldn’t be a static, “set-it-and-forget-it” task. As your operations grow more complex and new fraud tactics emerge, you’ll want to tweak your filters in response. Beyond that, you’ll also want to evaluate how specific filters impact your customer journey.
To choose the right option for your business, you’ll want to consider factors like:
The Downside of Fraud Filters
Poorly calibrated filters can lead to “customer insults,” or legitimate orders that are declined. The cost of these insults can far exceed the cost of actual fraud itself.
Reliance on poorly calibrated fraud filters can mean that you get a lot of false positives.
This means you might inadvertently reject a lot of orders that you shouldn't. After all, no one likes to be accused of wrongdoing, especially when no wrongs have been committed. Falsely flagging customers during legitimate transactions is a bad look, no matter how it happens. Customers who are put through this inconvenience are far less likely to return to your shop in the future.
Pre-transaction fraud prevention is just one part of a broader chargeback strategy.
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According to Visa’s 2024 Global eCommerce Payments & Fraud Report, about one in five eCommerce merchants surveyed report false positive rates above 10%. The majority reported false decline rates between 2% and 10%.
These false declines, aptly called “customer insults,” can discourage legitimate customers from doing business with your store and cause you to lose out on revenue you could’ve captured. In fact, false declines could be as much as 75 times costlier than true fraud. This means that overly aggressive fraud filters could be doing far more harm than good for your store.
Other Common Fraud Filter Mistakes That Merchants Make
Common fraud filter errors include relying on single-point filters, using outdated rules, or ignoring analytics that reveal high false decline rates.
Obviously, the intent behind fraud filters is protection. The trap, however, is that merchant errors may cause these tools to do more harm than good. Common pitfalls include:
Fraud Filter Costs & ROI: What to Expect
So, what kind of return on investment can you expect? Well, that depends on a few factors, including labor costs, accuracy, and the number of transactions flagged for review.
Fraud filter pricing typically follows one of three models: flat monthly fees for predictable budgets, tiered plans based on transaction volume, or per-transaction success fees. That said, the sticker price is only one part of the true cost of the system. To calculate the actual cost of your investment, you’ll want to account for hidden costs, including the labor costs associated with manual reviews and the lost lifetime value of customers hit by false declines.
In fact, manual review labor can often become more expensive than the software itself. If your team spends several hours per week investigating gray-area orders, moving from basic rules-based filters to a machine learning-based solution that automates these decisions may end up being more cost-effective.
You’ll also want to calculate your ROI correctly. Keep in mind that chargebacks prevented is only one measure of return; minimizing false positives is just as important, if not more.
Say a fraud filter can correctly approve legitimate buyers who would have otherwise been flagged. In most cases, that tool will have paid for itself many times over if it can prevent fraud while also allowing you to reduce your cart abandonment rate by just one percent.
Optimizing & Maintaining Your Fraud Filter Strategy
To keep your fraud filters effective, you’ll want to audit rules quarterly, adjust thresholds during peak seasons, track a broad basket of metrics, and block specific device fingerprints rather than entire geographies.
As mentioned before, static fraud filters are bound to become ineffective. For best results, you’ll want to audit your filter settings at least quarterly, while monitoring your baseline health metrics weekly or monthly.
Likewise, you’ll also want to adjust your filters during peak seasons or flash sales. During these windows, customers may buy more impulsively and from different locations. For this reason, you’ll want to loosen your velocity limits to prevent unnecessary false declines, while simultaneously tightening identity verification for high-ticket items to capture opportunistic fraud.
To determine whether your adjustments are moving the needle, you’ll want to track several different metrics, including your:
- False Positive Rate: This is the percentage of legitimate customers blocked. Tracking this metric can help indicate whether your rules are too aggressive.
- Fraud Catch Rate: This is the percentage of fraudulent transactions that were correctly flagged and declined. It measures the effectiveness of your current fraud settings.
- Manual Review Volume: This is the total count of orders requiring human intervention. A spike here suggests your automation logic needs to be recalibrated to handle new traffic patterns.
- Authorization Rate: This is the percentage of transactions successfully cleared by your gateway. A low authorization rate means that friction could be damping the buyer experience.
New fraud patterns are constantly emerging. When you notice a new pattern, avoid the temptation to throw the kitchen sink at it by blocking entire regions or IP ranges. Instead, implement a temporary filter that targets the device fingerprint associated with the attack.
And, as your transaction volume grows, work with your payment processor to integrate pre-authorization filtering. This stops fraud before the transaction is even sent to the bank, which can help you save on processing fees and keeps you in good standing with your acquirer.
Machine learning tools base their decisions on past decisions. Your data gets more and more inaccurate over time, leading to more false positives and more fraud.
Fraud Filters Require the Right Strategy
Like we said, fraud filters are essential tools that work best in tandem with one another. They need to be integrated into a broader fraud and chargeback prevention strategy. Here are a few tips to get you started:
#1 | Optimize Fraud Tools
The more fraud filters you use, the better protected your business is against criminal fraud. However, be aware that overdoing it can backfire too. The best advice we have here is to take inventory of your business, analyze your data closely, and identify your needs in comparison with your weaknesses.
Don’t have access to this information? Then it’s time to add internal analysis to your list of needs.
#2 | Best Practices Win
No one is perfect. Every business is run by humans and is, therefore, prone to error. This is true no matter how slick your fraud management systems might be.
Your best bet to prevent disputes and swiftly identify fraud is to examine your business policies and practices to single out trigger points that could be leading to fraud and chargebacks. Adopt best practices to eliminate potential triggers.
#3 | Perform ‘Red Flag’ Reviews
Software must be updated for a reason. If merchants neglect updates, they risk data breaches, damaging software glitches, and other easily-addressed complications that could be prevented.
#4 | Fight Back Against Chargeback Abuse
Fraud filters are meant to identify situations where a card is being used suspiciously. These technologies have no way to know if a given transaction will turn out to be friendly fraud later, though. On top of that, the fraudster is the actual cardholder, who may be a long-time, trusted customer.
Friendly fraud now accounts for nearly 60% of all chargebacks. Knowing what chargeback abuse looks like and how to respond can be tricky. However, ignoring the problem only allows it to get worse for everyone.
#5 | Know When You Need Help
A good strategy must use fraud filters to prevent criminal fraud and aim to limit intentional and unintentional post-transactional fraud. It also needs to tackle criminal and affiliate fraud and should include plans to relentlessly fight back against bad chargebacks and recover revenue.
Can your fraud prevention strategy do all that?
Chargebacks911® works closely with your team to create a comprehensive strategy customized for your business. Best of all, it’s backed by the industry’s only performance-based ROI guarantee.
A better solution for fraud prevention is at your fingertips. Contact us today to learn more about how we can optimize your fraud management efforts.
FAQs
What is fraud screening?
Fraud screening is the automated process of evaluating transactions in real-time to determine their fraud risk before approving or declining them. Fraud screening tools analyze dozens of data points — including payment card details, shipping address, device information, purchase history, and behavioral patterns — to assign a risk score to each transaction. Based on this score, orders can be automatically approved, declined, or flagged for manual review. The goal is to catch fraudulent transactions before you ship products or deliver services, protecting your business from chargebacks and revenue loss.
What is the best fraud detection tool?
There's no single “best” tool. The right solution depends on your business size, industry, and fraud profile. For small businesses, built-in platform tools (like Shopify's fraud analysis or Stripe Radar) provide solid baseline protection. Mid-size merchants often benefit from specialized solutions like Signifyd, Kount, or Riskified that offer chargeback guarantees. Enterprise businesses typically need comprehensive platforms like CyberSource or SEON that handle complex fraud patterns across multiple channels. The most effective approach combines automated screening with human review for edge cases and a chargeback management partner like Chargebacks911 to handle disputes that slip through.
What is the quickest way to detect fraud?
Real-time automated fraud screening is the quickest detection method, flagging suspicious transactions within milliseconds of order placement, before you ship anything. Modern AI-powered tools can analyze hundreds of data points instantly, comparing each transaction against patterns from millions of previous orders. However, “quickest” doesn't always mean “most accurate.” The fastest approach is to combine automated screening for obvious fraud with strategic manual review for borderline cases. For triangulation fraud specifically, the quickest detection comes from monitoring whether your products appear on third-party marketplaces at suspiciously low prices, which can alert you to being targeted before chargebacks arrive weeks later.
What's the difference between rule-based and AI-powered fraud filters?
Rule-based filters use fixed criteria you set manually (e.g., "decline all orders over $500 shipping to a different country than the billing address"). They're transparent and predictable but struggle with sophisticated fraud that doesn't fit your predefined rules. They also create lots of false positives as fraudsters learn to work around them.
AI-powered filters use machine learning to identify fraud patterns across millions of transactions, automatically adapting as fraud tactics evolve. They're better at catching novel fraud schemes and reducing false positives by understanding context and nuance. However, they're less transparent—it's harder to understand why a specific order was flagged.
The best approach is to use both. AI handles the complex pattern recognition, while rules catch known fraud scenarios specific to your business (like blocking repeat addresses from previous chargebacks).
How many legitimate orders get blocked by fraud filters?
The false positive rate varies widely — from 5% to 75% — depending on your filter settings and the sophistication of your fraud detection system. Industry research shows that merchants decline approximately 2.6% of all eCommerce orders due to fraud concerns, but up to 40% of those declined orders are actually legitimate. This represents billions in lost revenue globally. Modern AI-powered systems with machine learning typically achieve false positive rates under 10%, while aggressive rule-based filters can reject legitimate orders up to 30% of the time. The key is finding the right balance: too strict and you lose good customers; too lenient and you face excessive chargebacks.
Can fraud filters prevent chargebacks?
Fraud filters can prevent fraud-related chargebacks by stopping fraudulent transactions before they complete. However, they don't prevent all chargebacks. Fraud filters won't stop friendly fraud, merchant error chargebacks, service disputes, or processing error disputes.
Additionally, overly aggressive fraud filters can increase chargebacks if legitimate customers get frustrated by declined orders and dispute the authorization holds on their cards. For triangulation fraud specifically, standard fraud filters often fail entirely because they're screening the wrong transaction. The fraudster's buyer is legitimate; the fraud happens when the scammer orders from you.
