How AI is Revolutionizing Payment Fraud Prevention
In contrast to traditional rule-based programs, machine learning models self-update to counter new fraud patterns, whereby synthetic identity frauds, account takeovers, tests on cards, etc., are detected with accuracy exceeding 95%. Fraud detection solutions like Forter and Riskified delve deeper into the identification of subtle discrepancies in a customer’s typing speed, mouse movement, or device orientation that a human reviewer may have missed. At the same time, they keep down false declines—something that stands at $443 billion in 2023—by confirming their identity based on purchase history and web behavior. In high-risk industries, AI solutions can connect with payment gateways like Stripe and Adyen custom-calibrating risk scores for each transaction and automatically approving low-risk orders.
Implementing AI Fraud Prevention Without Slowing Checkout
The most useful artificial intelligence solutions for fraud behave like background commerce partners and only jump in when risk thresholds are exceeded. For example, the real-time decision engine-Kount might look at billions of data points for its fraud networks all over the globe to establish a transaction against possible suspicious IPs, emails, or payment methods associated with past chargebacks. Sensitivity might be defined by merchant tolerance: for example, very high-end retailers activating all orders over $1,000 might switch on 3D Secure for those groups, whereas sellers of digital goods do not appreciate any frictions with seamless flows. Importantly, those systems become more accurate over time with more orders, as they learn from feedback from merchants. Some providers even back refunds against the AI’s wrong approval of fraud transactions.
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