COMPANY A REDUCED FALSE POSITIVES BY 97.3% WITH HAWK AI AUTO-CLOSING
More signal, less noise
Throwing people at compliance problems is an unsustainable solution, particularly as a business grows. Scaling headcount is an expensive, complex bottleneck that masks an underlying technology problem.
Company A, a global payments processor that handles over one billion payments per year in more than 60 markets, uses modern AML technology powered by explainable AI to solve that problem. While already using Hawk AI’s rules and anomaly detection AI to find suspicious activity and file SARs, Company A wanted to further improve the effective detection of financial crime without placing an additional burden on their teams.
Now, Company A enjoys the results of False Positive Reduction and Auto-Closing: a 97.3% reduction in false positives, reasonable thresholds, improved case relevance, manageable caseloads, explainable auto-closing, a risk-based approach, and quality control.
Scalability and Results
>1 Billion Transactions
cross-border payment processing
97.3% False-positive reduction
versus standard rules
"Rather than intentionally limiting cases for review based on team capacity, Company A can let as many cases as possible be generated; the False Positive Reduction model functions as a hyper-productive 'virtual' team member that automates the first line of case review."
BUILT IN GERMANY WITH DEEP INDUSTRY KNOWLEDGE
AI helps your exports spot anomalies and detect unknown crime patterns
70% false positive reduction, your team can focus on priority tasks
All stakeholders can easily understand and trust out system's results
EASY TO DEPLOY
Built with deployment options that can be customised to your needs.
Monitor any transaction for red flags using a comprehensive set of rules in combination with Behavioral Analytics.
Screen customers against Sanctions, PEP, watchlists, and adverse media during onboarding and thereafter.
Customer Risk Rating
Dynamically score customer risk using internal and external data. Add behavioral analytics for richer context.