Free up your analysts
Eliminate False Positives by modeling good client behavior, allowing analysts to focus on critical cases.
Cost-effective monitoring
Make your Transaction Monitoring more cost-effective with our effective and easy to integrate AML or Fraud models.
Easy implementation
Our solution generates explainable and transparent AML or Fraud models, in the code language needed for your Transaction Monitoring system.
This is how it works
Know good, catch bad
Struggling with excessive False Positives? Can’t catch all suspicious transactions with your existing system? Traditional banking rules can no longer keep up with the sophisticated techniques used in money laundering and fraud. Learn how we deal with these challenges for you by watching our video.
Eliminate around 80% False Positives
Catch up to 300% more financial crimes
Improve efficiency updating/training models by 90%
AI models for Anti-Money
Laundering (AML) and fraud
At Sygno, we model the good behavior of your customers to highlight bad behavior, making Transaction Monitoring more efficient and effective.
Sygno’s leadership has decades of experience in Risk and Compliance
With our industry expertise and extensive experience in implementing Transaction Monitoring systems, we can provide a tailored solution for your TM environment of choice. Sygno offers transparency for the risk owner, (AML) compliance officer, auditors and regulators.
Sygno Analytics
Automated Machine Learning in Transaction Monitoring system
Sygno Analytics for your TM system
If you’re looking for a solution that can optimise your Transaction Monitoring system, learn more about our solution Sygno Analytics.
Bank – AML case
• Not fully compliant on all risks
• Risk of missing too many unusual transactions
• Complex legacy rulesets
• Too many False Positives
The bank could shift from individual rule definitions to a more holistic model, by configuring all risks into the Sygno AutoML solution for their new AML model. This Increased their risk coverage and at the while also reducing their existing ruleset complexity. The bank can now use the Sygno model, which focuses on good client behavior, to reveal previously unknown suspicious transactions (anomaly detection). The use of the Sygno model also resulted in a huge False Positive reduction, while at the same time an increase in potential SAR filings.
Bank- Fraud case
• Increase the pro-active and early fraud detection
• Manage time-to-market of models for new payment and fraud types
Sygno modeled the good or normal customer behavior of the clients of this bank, which resulted in anomalies that clearly sticked out. Sygno generated a more robust predictive model in fraud detection, which supported the bank inearly detection of emerging fraud patterns. Recognizing the “normal” behavior of the bank’s best customers reduced the False Positives substantially and improved the client experience, especially for those engaging in irregular or international payments. Time-to-market for the new model, and updates, is much faster (a couple of hours).
Credit Union- AML case
• Limited to no inhouse data science capability
• External models not effective for local risks and customers
By configuring all risks into the AutoML solution of Sygno, credit unions can ensure coverage of their local risk requirements, making the generated model applicable and effective for meeting their regulatory standards. Integrating these models into existing transaction monitoring systems allows for quick benefits without complex migrations, emphasising the importance of the right logic for identifying suspicious transactions. This model supports False Positive reduction and detection of more unusual transactions.
Ready to enhance your Transaction Monitoring process?
Contact us today for a free consultation and discover how our advanced Machine Learning model can boost your AML and Fraud detection.