Dynamic Transaction Monitoring with machine learning
TW2.0 brings you Dynamic Transaction Monitoring - a sophisticated transaction monitoring capability enhanced with machine learning and advanced analytics to identify anomalies. Complement traditional rules-based reviews of customer activity with an independent machine learning driven review of your customer’s activities.

Create, update and test rules instantly with Sandbox
Say goodbye to complex rule coding, or waiting months just to modify or configure new rules and scenarios. TW2.0 has an innovative Sandbox which puts you in control. You can build your own rules from an easy-to-use rule palette, then test and deploy rapidly. The flexibility means you can continually improve your transaction monitoring processes to align with policy and reduce false positives.

Automatically review customer behaviour
A single transaction in isolation is often meaningless without the customer’s context. Customer behaviour, and how it compares to onboarding information, gives more insight into early signs of nefarious behaviour. The TW2.0 Client Activity Review monitors a customer’s behaviour and alerts you if this changes. This creates a profile so your team can efficiently measure risk and detect potentially suspiciousclient behaviour.

Know customer risk in real-time
A risk-based approach requires a real-time picture of risk levels. In TW2.0, Risk-based Scorecards produce a risk score for each customer. When used with Dynamic Transaction Monitoring, the Risk-based Scorecard gives you an additional powerful barometer to measure customer risk.

Configure your dashboard for efficiency
In TW2.0 monitor data that is only relevant to your workflow and tasks with the new highly configurable dashboard. With access to a palette of over 20 widgets, you control your view of the dashboard to see only the data that’s important to you.