The rules within the Automated Rules Engine are continuously tuned to ensure that they are still highly effective and accurate, reducing manual review time and enabling risk teams to maintain rule effectiveness.
Rules engines are part to many companies’ online fraud detection and anti-money laundering infrastructures. Unsupervised machine learning catches evolving attacks by correlating user and event attributes of coordinated attack campaigns without rules or labels. The Automated Rules Engine then uses the results of the UML Engine to create human-readable rules around them, automatically adapting to these evolving attacks.
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