The Product

Data Integration uses trading, back office, risk and access control systems. Cardabel uses direct integration with no staging and no data mapping. Users will recognize their data and fraudsters will have less opportunity to manipulate intermediate data.
Cardabel anomaly prediction engine includes a set of machine learning services dedicated to catch a wide spectrum of fraud and operational issues. These services catch both known and new types of anomalies that have not yet been identified or used in the market. The dynamic pattern generation and the subsequent recognition make it impossible for a fraudster to work around the detection.
This interface provides Risk and Control users with a dashboard detailing trade anomalies and the source of each anomaly. All anomaly life-cycle events are supported in order to allow users to decide whether the anomaly is a fraud, an operational issue or a false alarm. Users can take action on it accordingly. A clear navigation and an investigation of data allow users to collaborate on and determine the true status of predicted anomalies.
This application assists risk and data scientists in the complex tasks of preparing the data consumed by the anomaly prediction engine and the fine tuning of the engine parameters. Once defined, the workflows are automatically executed by the prediction engine. The process of integration mapping is reduced to a bare minimum so that users recognize their data when analyzing the predictions. This type of integration also speeds the initial integration process, from months to weeks. Clear definitions of each type of workflow associated with each algorithm allow data scientists and risk managers to focus on added-value tasks.
Cardabel is entirely Software-as-a-Service, implemented on a secure and high availability platform. There is much less opportunity for internal fraudster to manipulate the predicted anomalies or the audit logs of the life cycle. SaaS installations use a much smaller footprint and require fewer IT resources. Finally, the scalability of the service is automatic. There is no need to adjust IT Infrastructure as the need for additionnal IT resources increases.
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