SmartIPX have built an impressive toolkit for fraud mitigation. Our ongoing investment in Cataleya’s Orchid One eSBC is now set to reap further rewards.

Orchid One’s new Machine Learning (ML) algorithm is enabling a more proactive approach to monitor any ongoing call flow.

Orchid One is able to capture network data and analyse it in real-time by monitoring the traffic flow on the network and how the sessions are performing.

As most of the current SBCs capture only the historic data like the call data records (CDRs), Orchid One differentiates itself by being able to match historic data with real-time analytics to deliver a new level of network visibility capable of identifying fraudulent activity and increasing the ability to prevent fraud.

This toolkit brings interconnect management out of the ‘build and forget’ world of TDM and changes a rate limited view to assuring a smart or intelligent voice network right for business, combining insight and transparency with machine learning. Placing the spotlight on unacceptable criminal practices will make it simpler for interconnect to be done in a market where margin pressure is intense.

This new functionality, backed up by our engineers knowledge of our partners’ network and traffic patterns, and the processes to make sure that this knowledge is acted upon swiftly and decisively, makes it even easier for us to detect anything suspicious or abnormal in call behaviour.

SmartIPX CEO Paul Tindley describes this technology advancement:

“…as if we have been given a set of golden gloves as we get on the front foot in this battle to combat fraud, which is as our quarterly fraud reports demonstrate, is now believed to be bigger than narcotics crime.”

Learn more how we connect our clients to cloud-centric networking

 Telecoms is in transition to a full cloud environment,   where rapid deployment, reliability and technology   upgrades have become a de facto standard. This   opens up a wealth of service possibilities not feasible   under more traditional siloed telecoms scenarios.

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