Imagine trying to buy a new cell phone, an international ticket, or a special gift — and having your transaction flagged as suspicious and blocked by a fraud prevention system, without any plausible explanation. That’s the downside of online shopping. While these systems have been designed to protect against fraud and ensure a satisfactory shopping experience, they can also cause frustration and losses.
With the exponential increase in data collection and sharing, 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 proving costly — not just in revenue, but also in reputation. It’s what we call false positives, when a legitimate transaction is incorrectly identified as fraudulent.
The hidden cost of excess security
Modern fraudsters operate like businesses: they are fast, organized, and fueled by large volumes of data. Techniques like “phishing as a service” simulate identities from leaked information and exploit behavioral loopholes in systems. They no longer follow obvious patterns, making 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 react. It’s a rigid and ineffective model — the shopping experience is compromised, conversion rates plummet, and customer loyalty is lost.
And the impact goes beyond: 32% of consumers who go through a false positive abandon the merchant forever. A single failure in the anti-fraud system can mean the definitive loss of revenue and reputation. According to Javelin Strategy & Research, these errors already cost retailers in the United States $118 billion per year — 13 times more than actual fraud losses. The math doesn’t add up.
The importance of real-time intelligence and behavioral analysis
To deal with 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 analysis to make precise decisions without compromising the user experience.
With algorithms that continuously learn, it is possible to understand individual patterns: location, time, device, purchase history, and payment method. Behavior speaks louder than any pre-programmed rule.
It’s not just a matter of saying ‘yes’ or ‘no,’ but interpreting the context. The same customer can buy something in São Paulo in the morning and in Rio de Janeiro at night. They might switch phones, change browsers, or update the 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 is normal for each user and identify deviations — without relying solely on pre-defined rules. A study from MIT with data from a European bank showed that this strategy reduced false positives by 54%, resulting in savings equivalent to $220,000.
The future of invisible authentication
The combination of AI and user profiles to provide more accurate recommendations — along with the use of data to balance security and conversion — opens doors to new technologies. One of them is the vector identifier: a solution capable of detecting fraud even when the attempt comes from devices with clean cookies or in anonymous mode. But legitimate users can also act this way.
And when both fraudsters and good users hide behind the same mask, how to differentiate them? By combining vector data with the device’s ‘fingerprint,’ the system can understand the typical behavior of that user and better detect anomalies. This significantly increases accuracy, avoiding unnecessary blocks without compromising security.
In this model, small variations are treated with contextual intelligence — used to detect anomalies based on the expected user pattern. Subtle changes (like a software update) do not trigger alerts, but significant alterations (such as changing operating systems or geolocation) can be flagged if they are outside the usual behavior. This is the new frontier of security: acting in the background, without friction. The best anti-fraud system is the one the customer doesn’t even notice.
Security that drives sales, not the other way around
Companies tend to believe it’s better to reject some legitimate transactions, even if it slightly reduces conversion rates, than to face the consequences of fraud. But they don’t need 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 do not need to be opposing forces – they should go hand in hand. The secret lies in precision, not rigidity.
The era of false positives requires companies to invest in intelligent technologies, such as AI, behavioral analysis, and advanced fraud detection tools. These innovations reduce losses without sacrificing legitimate sales – and, most importantly, without driving away customers.
Security and customer experience are not opposites – when done right, they go hand in hand. Providing protection is mandatory. But doing so without compromising the experience is what truly makes a difference in today’s increasingly competitive market.
By Thiago Bertacchini, Sales Head of Nethon
