The Green Sheet Online Edition
February 9, 2026 • 26:02:01
Stop low-intent fraud at email capture
AtData introduced Gibberish Detection, a new machine learning–driven capability designed to help organizations identify low-intent, automated or synthetic email addresses at the point of capture. The feature expands AtData's fraud prevention suite by providing a fast, high-confidence signal that helps fraud and risk teams reduce exposure while improving decision speed and operational efficiency, AtData stated.
Email address capture remains a critical early step in digital identity and account creation, yet it is increasingly targeted by bots and automated tools generating nonsensical or fabricated inputs.
According to AtData's activity network, approximately 5 percent of captured email addresses exhibit characteristics consistent with gibberish or randomized generation. In a recent deployment with a global on-demand services provider, that rate climbed to nearly 10 percent of new orders, signaling elevated automated activity at the top of the funnel, AtData noted.
Gibberish Detection evaluates the structure and composition of an email address in real time, analyzing indicators such as anomalous patterns, randomness and behaviors commonly associated with bots. The model produces a confidence-weighted signal that can be used immediately within identity verification, fraud screening and risk decisioning workflows.
"Stopping automated and synthetic accounts at the first touchpoint is one of the most cost-efficient ways to lower fraud exposure," said Diarmuid Thoma, head of fraud and data strategy at AtData. "Gibberish Detection converts messy email input into a structured signal that boosts model accuracy, keeps review queues focused on real risk, and slows fraudsters instead of valuable customers."
By surfacing a purpose-built signal early in the customer journey, the capability enables faster decisions with less friction, AtData pointed out. Organizations can block or score low-value registrations instantly, reducing the need for additional verification steps that often disrupt legitimate users.
The model also improves accuracy by complementing existing fraud signals, helping teams avoid over-reliance on rigid rules that can generate false positives, the company added, noting that operational efficiency is another key benefit.
By filtering out low-intent or automated registrations upfront, Gibberish Detection helps reduce manual review volumes, often one of the largest cost drivers in fraud operations, allowing teams to focus resources on higher-risk activity, AtData stated.
Gibberish Detection is available immediately through AtData's Fraud API endpoint, giving organizations a lightweight, scalable tool to strengthen digital trust and improve fraud prevention outcomes from the first interaction. For more information, please visit atdata.com.
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