Imagine trying to buy a new mobile phone, an international ticket or a special gift & having your transaction flagged as suspicious and blocked by a fraud prevention system, without any plausible explanation.That is the downside of online shopping. Although 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, the rapid digitization of increasingly sophisticated fraud systems and tactics, the market has hardened its defenses. But this move has created a paradox: trying to protect too much is getting expensive 'IT's not only in revenue, but also in reputation. It's what we call false positives, when a legitimate transaction is misidentified as fraudulent.
The hidden cost of over-security
Modern fraudsters operate like companies: they are fast, organized and powered by large volumes of data. Techniques such as “phishing as a service” simulate identities from leaked information and exploit behavioral loopholes in systems. They no longer follow obvious patterns, rendering traditional models obsolete and forcing companies to seek more robust layers of security.
While fraudsters innovate, many financial services and retail companies still rely on fixed rules to react. It is a rigid and ineffective model '''When 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 shopkeeper forever. A single flaw in the anti-fraud system can mean the definitive loss of revenue and reputation. According to Javelin Strategy & Research, these errors already cost U. S. retailers US$ 118 billion per year 13 times more than actual losses from fraud. The account does not close.
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 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.The behavior speaks louder than any pre-programmed rule.
It is not just about saying “sim” or “not”, but about interpreting the context. The same customer can buy something in Sao Paulo in the morning and Rio de Janeiro at night.He can switch phones, change browsers or update the operating system of the device.The anti-fraud system needs to understand this & not block the transaction.
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 DO without relying only 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 US$ 220 thousand.
The future of invisible authentication
The combination of AI and user profiles to offer more accurate recommendations, combined with the use of data to balance security and conversion, opens the door to new technologies. One of them is the vector identifier: a solution capable of detecting fraud even when the attempt departs from devices with clean cookies or in anonymous mode.
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 greatly increases accuracy, avoiding unnecessary locks without compromising security.
In this model, small variations are treated 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 operating system change or geolocation change) can be flagged if they are outside the usual behavior.This is the new frontier of security: acting behind the scenes, without friction. The best anti-fraud system is the one that the customer does not even notice.
Security that drives sales, not the other way around
Companies tend to believe that it is better to refuse some legitimate transactions, even if it reduces conversion rates a bit, than to suffer the consequences of a fraud.
Therefore, adopting a fraud prevention solution that balances security and convenience is a real market need. Security and user experience do not have to be opposing forces (they must go together. For this, the secret is in accuracy, not rigidity.
The era of false positives requires companies to invest in smart technologies such as AI, behavioral analytics, 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, go hand in hand. Offering protection is mandatory. But doing so without compromising the experience is what really makes the difference in today's increasingly competitive market.
By Thiago Bertacchini, Head of Sales at Nethon
