Imagine trying to buy a new cell phone, an international flight, or a special gift—only to have your transaction flagged as suspicious and blocked by a fraud prevention system, without any plausible explanation. This is the downside of online shopping. While these systems are designed to protect against fraud and ensure a satisfactory shopping experience, they can also cause frustration and loss.
With the exponential increase in data collection and sharing, the rapid digitization of systems, and increasingly sophisticated fraud tactics, the market has hardened its defenses. But this movement has created a paradox: trying to protect too much is costing you—not only in revenue, but also in reputation. This is what we call false positives, when a legitimate transaction is mistakenly identified as fraudulent.
The Hidden Cost of Over-Security
Modern fraudsters operate like businesses: they’re fast, organized, and fueled by massive amounts of data. Techniques like “phishing as a service” simulate identities based on leaked information and exploit behavioral flaws in systems. They no longer follow obvious patterns, rendering traditional models obsolete and forcing companies to seek more robust security layers.
While fraudsters innovate, many financial services and retail companies still rely on fixed rules to respond. It’s a rigid and ineffective model—the shopping experience is compromised, conversion rates plummet, and customer loyalty is lost.
And the impact goes even further: 32% of consumers who experience a false positive abandon the retailer forever. A single failure in the anti-fraud system can mean a permanent loss of revenue and reputation. According to Javelin Strategy & Research, these errors already cost US retailers $118 billion annually—13 times more than actual fraud losses. The math doesn’t add up.
The importance of real-time intelligence and behavioral analysis
To address this scenario, the new era of prevention requires intelligence, not excessive rigidity. This means using a combination of artificial intelligence (AI), real-time data, and behavioral analytics to make accurate decisions without compromising the user experience.
With algorithms that continuously learn, it’s possible to understand individual patterns: location, time, device, purchase history, and payment method. Behavior speaks louder than any preprogrammed rules.
It’s not just about saying “yes” or “no,” but about interpreting the context. The same customer can buy something in São Paulo in the morning and in Rio de Janeiro in the evening. They might change their phone, switch browsers, or update their device’s operating system. The anti-fraud system needs to understand this—and not block the transaction.
By applying machine learning techniques, companies can create models that learn from historical data and reduce false positives over time. The goal is to understand what’s normal for each user and identify deviations—without relying solely on predefined rules. An MIT study using data from a European bank showed that this strategy reduced false positives by 54%, generating savings equivalent to US$220,000.
The future of invisible authentication
The combination of AI and user profiles to offer more accurate recommendations—along with the use of data to balance security and conversion—opens the door to new technologies. One of these is vector identification: a solution capable of detecting fraud even when the attempt originates from devices with cleared cookies or in incognito mode. But legitimate users can also act in this way.
And when both fraudsters and good users hide behind the same mask, how can you tell them apart? By combining vector data with the device’s “fingerprint,” the system can understand that user’s typical behavior and better detect anomalies. This significantly increases accuracy, avoiding unnecessary blocks without compromising security.
In this model, small variations are handled with contextual intelligence—used to detect anomalies based on expected user patterns. Subtle changes (like a software update) don’t trigger alerts, but significant changes (like an operating system switch or a change in geolocation) can be flagged if they deviate from typical behavior. This is the new frontier of security: working behind the scenes, seamlessly. The best anti-fraud system is one the customer doesn’t even notice.
Security that drives sales, not the other way around
Companies tend to believe it’s better to decline some legitimate transactions, even if it reduces conversion rates slightly, than to suffer the consequences of fraud. But they don’t have to adopt this stance if they have the right tools.
Therefore, adopting a fraud prevention solution that balances security and convenience is a real market need. Security and user experience don’t need to be opposing forces—they must work together. To achieve this, the key lies in precision, not rigidity.
The era of false positives requires companies to invest in intelligent technologies such as AI, behavioral analytics, and advanced fraud detection tools. These innovations reduce losses without sacrificing legitimate sales—and, most importantly, without alienating customers.
Security and customer experience aren’t opposites—when done well, they go hand in hand. Providing protection is mandatory. But doing so without compromising the experience is what truly makes the difference in today’s increasingly competitive market.
By Thiago Bertacchini, Head of Sales at Nethon
