Artificial Intelligence Engine
Using Artificial Intelligence modelling the Zensed Engine is built to continually analyse and improve anti-fraud results and to monitor, react and adapt to new sources and trends in fraudulent activity. Thanks to ongoing learning and live input from global merchants we help you stay one step ahead.
Our advanced machine learning have analysed millions of global transactions to gain a deep insight in the reasons behind fraudulent customers. The Zensed machine learning technology scores every aspects of a transaction in real-time, giving you the advantage to act before it is too late.
Big Data Analytics
Zensed gain insights to millions of transactions, Using machine learning and artificial intelligence modelling with Big Data the Zensed platform is built to continually analyse and improve anti-fraud results and to monitor, react and adapt to new sources and trends in fraudulent activity.
Zensed collects certain customer data and all additional data which the client can send to the system. Zensed being the only company on the market that accepts additional data to be added to the system can give a clearer picture of how the transaction will result.
To gain extra insight in to a transaction, all data is enriched with hundreds of additional data points. Each of these data points have been handpicked to impact the most on the outcome of the transaction. The insights are clearly presented for each transaction so that the client can learn more from its customers.
Zensed Engine Analysation
The Zensed Artificial Intelligence Engine analyses all these data points and finds patterns and anomalies which helps the system determine if the transaction will be fraudulent or not. The Artificial Intelligence Engine will reinvent itself with every transaction to provide further accuracy in its results.
The result predicted by the Zensed system will be highly accurate and backed up by insightful information in the backoffice. For all transactions processed through the Zensed system the client can always follow up on historical transaction and see how the system has learnt from its previous analytics.