Simulate millions of potential scenarios in a cost-effective, GDPR compliant virtual environment to significantly improve your fraud detection systems.
Winning the battle against fraud
Regulators are increasingly expecting firms to monitor and report on the effectiveness of their fraud detection systems. But combating money laundering is an enormous task and comes with substantial costs and risks.
Historical data is not enough
Current approaches to fraud analytics are limited to historical data, which is not rich enough to develop better controls.
Evolving fraudulent behavior
The static nature of today’s controls means they often fail to detect the adaptive behavior of criminals.
Data privacy issues
It’s difficult to procure and get approval to use real transaction data due to privacy laws like GDPR.
High rate of false positives
Rules-based transaction monitoring systems result in a high number of (expensive) false positives.
Generate synthetic data
Create synthetic data that contains no personal customer information and is completely compliant with privacy regulations.
Reduce false positives
Use labelled data to generate information about missing fraud (false negatives) and calculate the real reduction of financial fraud.
Introduce machine learning
Introduce learning agents into the simulation to uncover fraud that has not even been committed yet.
Self-evaluate your system
Use simulation to add increasingly complex scenarios of fraudulent behavior and suspicious activity, then evaluate your controls.
Chief Executive Officer at Barclays Bank