Outperform: How Socure’s AI-Powered Global Watchlist Monitoring Revolutionizes Sanctions Screening

Compliance teams face growing sanctions-screening demands as legacy name-matching systems generate large volumes of false positives and require costly manual reviews, while identity-verification deficiencies account for a substantial share of suspicious activity reports. Socure presents its AI-powered Global Watchlist Screening with Monitoring solution as an alternative that combines entity resolution with a two-stage scoring process: an initial name-match score identifies possible matches, while an entity-correlation score evaluates identifiers such as addresses, birth dates, and national IDs to determine whether records represent the same person. The company reports that its approach improves screening accuracy by 20% versus competitors, reduces false positives by 30%, eliminates more than 70% of false positives through entity correlation, and cuts compliance resource costs by 60%. It also claims near-98% AUC performance for its deep-learning name matching models, a 99.2% resilience rate against name variations, and a 75% reduction in manual review time through automated case management and explainable recommendations. For politically exposed person and adverse-media screening, Socure says its NLP-based contextual analysis achieves 91% real-world accuracy and avoids irrelevant keyword-based matches, such as confusing sports references with violent-crime reporting.

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