Imagine trying to buy a new 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. This is the downside 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, rapid digitization of systems, and increasingly sophisticated fraud tactics, the market has tightened its defenses. But this movement has created a paradox: trying to protect too much is becoming costly — not just 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 excessive security
Modern fraudsters operate like businesses: they are fast, organized, and fueled by large volumes of data. Techniques such as ‘phishing as a service’ simulate identities using leaked information and exploit behavioral gaps 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 further: 32% of consumers who experience a false positive abandon the retailer forever. A single failure in the antifraud system can mean the 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 analysis to make precise decisions without compromising the user experience.
With continuously learning algorithms, it’s 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 interpreting the context. The same customer might buy something in São Paulo in the morning and in Rio de Janeiro at night. They might change phones, switch browsers, or update their device’s operating system. The antifraud 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 with data from a European bank showed that this strategy reduced false positives by 54%, generating savings equivalent to $220,000.
The future of invisible authentication
The combination of AI and user profiles to offer 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 cleared cookies or in anonymous mode. But legitimate users might also act this way.
And when both fraudsters and good users hide behind the same mask, how to 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 the user’s expected pattern. Subtle changes (like a software update) don’t trigger alerts, but significant changes (like switching operating systems or changing geolocation) can be flagged if they’re outside usual behavior. This is the new frontier of security: operating behind the scenes, without friction. The best antifraud 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 decline some legitimate transactions, even if it slightly reduces conversion rates, than to suffer the consequences of fraud. But they don’t need to adopt this stance if they have the right tools.
That’s why adopting a fraud prevention solution that balances security and convenience is a real market necessity. Security and user experience don’t have to be opposing forces — they should go hand in hand. For this, the secret lies in precision, not rigidity.
The era of false positives requires companies to invest in intelligent technologies like AI, behavioral analysis, and advanced fraud detection tools. These innovations reduce losses without sacrificing legitimate sales — and, most importantly, without driving customers away.
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, Head of Sales at Nethon
