eyeDES® platform is used to run Machine Learning models developed by Features Analytics, and additionally it can also run predictive models developed by your organization or by third parties.
The platform offers a real-time processing system that allows to detect fraudulent activity at the moment of purchase by running the predictive models developed on your historical data. When designed by our team, we develop not one single model but multiple models to address different data segments and business channels within your organization.
Once ready, the models are immediately deployed on the eyeDES® platform to score transactions or events in milliseconds. Reasons-why are delivered in the same time as the transaction score. Model governance and deployment that usually takes weeks or months, is done on the eyeDES® platform in seconds or minutes. We have streamlined the process, made it easy and cost effective.
Model deployment
Without eyeDES
With eyeDES
The platform is equipped with built-in automatic model refresh. Once the first models are installed, the model retrain will be done automatically by the platform, in a fully autonomous way.
Organizations can use the eyeDES® dashboard and reporting tools to monitor the performance of the payment and business operations. The dashboard can be accessed via a web user interface providing visualization of charts and metrics that can be adapted to fit each firm's specific needs.
Solutions
Real-time or batch fraud detection and prevention for banks, processors, acquirers and merchants.
Capabilities
Fully configurable and scalable platform. Multiple pipelines executed simultaneously (production, shadow, A/B testing). Processes thousands of txn per sec with ms response time.
Integration
Flexible platform integration. On-Premise the platform can be deployed as a standalone server or an embedded library via our available API’s. On-Cloud available as a service.
Intelligence
Automatic model refresh and configurable dashboard for monitoring the system activity, the detection performance and any additional business KPI’s.
ML Case Study