EXECUTIVE INSIGHT ATDATA
Diarmuid Thoma, Head of Fraud Prevention and Data Strategy, AtData signals to spot patterns and anomalies associated with fraud minutes or seconds after they appear. Machine learning models retrain on new signals and feedback, so detection keeps pace with evolving tactics like synthetic identity creation and account takeover. Email intelligence scores addresses for age, domain reputation, disposability, compromise indicators, aliasing and behavioral engagement, giving early context that transaction-only systems miss.
Combining email signals with model outputs helps distinguish first-party fraud from third-party fraud and other abuse types, which improves the accuracy of front-line decisions. Scores feed decisioning engines to apply tiered responses, from silent monitoring to stepped-up authentication or automated decline, reducing losses while preserving customer experience. Rich email context reduces unnecessary friction by separating legitimate edge cases from true risk, improving conversion and operational efficiency.
This combination empowers fintechs to detect and stop fraudulent activity before it harms customers or businesses.
fintechmagazine. com 139