Transaction Monitoring

Get insight into your customers and their behaviour with intelligent transaction monitoring

Stay ahead with our real-time transaction monitoring solution, which allows you to manage the detection, investigation and monitoring of suspicious transactions.


What this means for you

Flexible rule-based monitoring combined with the power of machine learning

Our approach to transaction monitoring includes utilising a combination of rule-based monitoring, supervised learning and unsupervised learning. This gives you the ability to include traditional rules that create an event based on a predefined scenario, while simultaneously leveraging machine learning.

The supervised machine learning models are trained off either existing or simulated data, while unsupervised machine learning allows for automated hyper segmentation, anomaly detection and cluster analysis.

Utilising a comprehensive rule set that monitors your transaction data based on suspicious activity patterns means that you can identify high risk transactions as they occur, or monitor these retrospectively through our replay feature.

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Gain a holistic view of your customer's behaviour

Only by gaining a holistic view of customer behaviour, can you make faster, more informed decisions. Explore related transaction data and gain clearer insight into the risk patterns of all parties involved.

By consolidating associated alerts, you can reduce the volume of alerts your team has to review and leverage historical activity to spot patterns before they turn into risk.

Real-time, AI-powered monitoring

Detect indicators of potential money laundering in real-time, with our AI-powered stream-based solution, which is tailored to your particular business needs. Our solution continually identifies unusual financial activity with ease, allowing you the opportunity to further investigate only truly suspicious behaviour.

Combine human and AI capabilities to minimise risk, while maximising efficiency and leveraging our AI-based entity resolution technology to link customer profiles across different data sets and better understand transaction flows using link analysis.

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