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Argyle Data Extends Predictive Analytics Offerings to Enterprise Data Centers

Argyle Data has expanded its core machine learning and AI application suite to engage with clients in enterprise areas including IoT security, financial services and online/mobile banking.

Our approach is fundamentally different, a breakthrough in the field of machine learning, and extensible to all network types and configurations” said CEO Vikash Varma.  “The ROI is proven. Our achievements in subscriber validation, credit checking and fraud detection for communications service providers have created a natural expansion path to financial services. Financial services firms and large enterprises encounter many of the same issues as telecommunications providers.”

In 2017, Argyle Data will apply its expanded application suite to address a broader market. Enterprises are increasingly turning to machine learning/AI applications to drive predictive analytics, as big data analysis evolves into a key business tool. In addition to its live telco deployments in the U.S. and Europe, Argyle Data is engaged in ongoing trials and proofs of value in enterprise and financial services applications.

The move comes as banking and financial services organizations increasingly seek cutting-edge analytics and authentication solutions to offset growing levels of online and electronic fraud.  The global fraud detection and prevention (FDP) solutions market is estimated to reach U.S.$33.19 billion by 2021, driven by a steady increase in online and electronic transactions and associated rises in fraud levels. New research indicates that authentication and analytics solutions such as Argyle Data’s are expected to dominate the FDP market from 2016 to 2021.

Argyle Data’s approach to network analytics incorporates significant developments in the field of machine learning. Unlike other machine learning/AI offerings, Argyle Data’s layered, adaptive technology uses both supervised and unsupervised machine learning in combination to integrate feature sets containing both continuous and categorical attributes, which increases accuracy and reduces the likelihood of false positives.

This enables the near-instant uncovering of insights hidden within big data, without having to constantly program computers on what to look for or where.

Our application is by nature predictive, able to accurately pinpoint the likelihood of behaviors that fall outside the norm,” said Padraig Stapleton, VP of Engineering at Argyle Data.  “This is a substantial advance on detecting types of activity that are based on known fraud patterns.”

 

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