Chargebacks have previously been treated as an unavoidable cost of doing business, a byproduct of fraud, customer disputes, or payment errors that merchants simply react to after the fact. But as transaction volumes rise and fraud tactics grow more sophisticated, this reactive model is proving unsustainable. As we approach 2026, leading retailers attending the Fraud Prevention Summit are shifting strategy: moving from defence to prevention, powered by AI and behavioural analytics…
The Shift Toward Proactive Risk Management
Traditional fraud systems rely on static rules and post-transaction reviews to identify suspicious activity. By the time a chargeback is filed, the damage, financial and reputational, is already done.
Modern fraud prevention platforms use machine learning, device intelligence, and real-time behavioural analysis to flag and block high-risk transactions before authorisation. These systems analyse thousands of data points in milliseconds, assessing everything from device fingerprint and IP reputation to velocity patterns, past behaviour, and biometric signals.
The result is a far more dynamic approach that stops fraudulent transactions at the source, protecting revenue without adding unnecessary friction for genuine customers.
Behavioural Analytics: Understanding Intent, Not Just Identity
Behavioural analytics has become one of the most powerful tools in the anti-fraud arsenal. By studying how customers interact with a website or app (mouse movements, typing speed, navigation patterns) AI can detect subtle anomalies that indicate potential fraud.
For example, a genuine customer may browse products at a steady pace and use autofill for payment details, whereas a fraudster using stolen credentials may paste information rapidly and skip verification steps. Machine learning models trained on these behavioural patterns can instantly distinguish between the two and trigger adaptive responses.
This level of insight allows merchants to identify and stop chargeback risk before it even enters the payment flow.
Device Intelligence and Continuous Authentication
Combining behavioural data with device intelligence provides an additional layer of protection. Device fingerprinting can recognise returning users and detect mismatched or spoofed devices, while continuous authentication validates that the same legitimate user remains active throughout the session.
This integrated approach not only blocks high-risk activity but also reduces false declines, ensuring loyal customers enjoy seamless transactions.
AI-Powered Fraud Prevention as a Growth Enabler
By reducing friction and improving authorisation rates, merchants can enhance customer trust and boost conversions. As AI models continue to learn and evolve, prevention will increasingly outpace defence. The future of fraud management lies in understanding behaviour, not just transactions, and in stopping threats before they even begin.
Are you searching for Chargeback solutions for your organisation? The Fraud Prevention Summit can help!
Photo by Vitaly Gariev on Unsplash
