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, with no plausible explanation. This is the negative side of online shopping. Although these systems were 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, the rapid digitization of systems, and increasingly sophisticated fraud tactics, the market has hardened its defenses. But this movement created a paradox: trying to protect too much is becoming costly — 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 oversecurity
Modern fraudsters operate like companies: they are fast, organized, and fueled by large volumes of data. Techniques like "phishing as a service" simulate identities using leaked information and exploit behavioral vulnerabilities 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 respond. It is 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 experience a false positive abandon the merchant forever. A single failure in the anti-fraud system can mean the permanent loss of revenue and reputation. According to Javelin Strategy & Research, these errors already cost US retailers $118 billion a year — 13 times more than the actual losses from fraud. The account doesn't add up.
The importance of real-time intelligence and behavioral analysis
To deal with this scenario, the new era of prevention requires intelligence, not excessive rigidity. This means using a combination of artificial intelligence (AI), real-time data, and behavioral analysis to make accurate decisions without compromising the user experience.
With algorithms that learn continuously, it is possible to understand individual patterns: location, time, device, purchase history, and payment method. Behavior speaks louder than any pre-programmed rule.
It's not just about saying "yes" or "no," but about interpreting the context. The same customer can buy something in São Paulo in the morning and in Rio de Janeiro at night. He can change his phone, switch browsers, or update the device's operating system. The anti-fraud system needs to understand this — and not block the transaction.
By applying machine learning techniques, companies can create models that learn from historical data and reduce false positives over time. The goal is to understand what is normal for each user and identify deviations — without relying solely on predefined rules. A MIT study with data from a European bank showed that this strategy reduced false positives by 54%, resulting in savings of approximately US$ 220,000.
The future of invisible authentication
The combination of AI and user profiles to provide more accurate recommendations — coupled 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 can they be distinguished? By combining vector data with the device's "digital fingerprint," the system can understand that user's typical behavior and better detect anomalies. This significantly increases accuracy, preventing unnecessary blocks without compromising security.
In this model, small variations are handled with contextual intelligence — used to detect anomalies based on the user's expected pattern. Subtle changes (such as a software update) do not trigger alerts, but significant changes (such as an operating system switch or a change in geolocation) may be flagged if they are outside the usual behavior. This is the new frontier of security: working behind the scenes, 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 that it is better to decline some legitimate transactions, even if this 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 necessity. Security and user experience do not have to be opposing forces — they should go hand in hand. For that, 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 alienating customers.
Security and customer experience are not opposites — when done well, they go hand in hand. Offering 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 Director of Nethon
