Historically, retailers attending the Fraud Prevention Summit have relied on rules-based fraud detection systems to protect against chargebacks, account takeovers, and payment fraud. These systems typically flag transactions based on pre-set criteria, such as purchase value, IP mismatch, or card verification failures. While effective at blocking obvious threats, they often create as many problems as they solve: high false-positive rates, manual reviews, and friction for genuine customers…
But the industry is undergoing a decisive shift. Retailers are moving from static rule sets to AI-powered risk scoring platforms, which assess each transaction dynamically using advanced analytics and behavioural insights.
The Limits of Rules-Based Systems
Traditional systems operate like a traffic light: if a transaction ticks too many ‘red flag’ boxes, it’s stopped. But fraudsters evolve quickly, testing and bypassing known rules. Meanwhile, legitimate customers who happen to trigger these conditions, such as making a high-value purchase while travelling, are often blocked, creating frustration and lost revenue.
Rules-based approaches also require constant manual updating by fraud teams, leading to operational inefficiencies and slower response to new attack patterns.
AI-Powered Risk Scoring
By contrast, AI-driven platforms use machine learning models trained on vast datasets of historical and real-time transactions. Instead of a binary yes/no, they assign each transaction a risk score, reflecting the likelihood of fraud.
These platforms analyse hundreds of signals simultaneously, including device fingerprinting, behavioural biometrics, purchase history, and geolocation. For example, AI can detect whether a shopper’s typing cadence matches their usual profile, or whether a device has been linked to multiple identities.
This approach allows retailers to set thresholds that minimise false positives: accepting low-risk transactions instantly, flagging medium-risk ones for additional verification, and blocking only the highest-risk activity.
The Role of Behavioural Analytics
Behavioural analytics adds another layer of sophistication. Fraudsters may fake credentials, but they struggle to replicate natural customer behaviours—like browsing patterns, navigation flows, or payment method preferences. AI systems trained to spot these nuances can often distinguish genuine users from fraud attempts more accurately than static rules.
Business Impact in 2025
Retailers adopting AI-driven fraud platforms are seeing tangible benefits:
- Reduced false declines, preserving revenue and customer trust.
- Lower operational costs, as fewer manual reviews are required.
- Faster response to emerging fraud types, thanks to continuously learning models.
For UK retailers, where customer experience is as critical as fraud prevention, the shift from rules to risk scores represents somewhat of a strategic transformation.
In 2025, fraudsters are more sophisticated than ever, but so are the tools to fight them. Retailers that embrace AI-driven risk scoring are finding the balance between security and seamless shopping, positioning themselves to outpace both fraud and competitors.
Are you searching for AI solutions for your organisation? The Fraud Prevention Summit can help!
