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Argyle Data Real-time Fraud Detection

Solutions Gallery > Argyle Data Real-time Fraud Detection

Solution overview

Revenue threats are currently a $38 billion criminal business.

Argyle Data has had the privilege of working with global leaders and visionaries on their strategies for revenue threat analytics, big data, and machine learning. What consistently comes up is that best-in-class carriers know the revenue threats that they have been attacked with in the past. What they don’t know is how to prepare for future attacks that will likely incorporate new types and methods of revenue threats.

Leading mobile operators understand that data is a massively underutilized asset, and they understand the enormous potential revenue impact of big data analytics and machine learning. That’s why the leading mobile operators partner with Argyle Data to gain visibility into the revenue threats and attack patterns being waged against their networks – threats that, on average, cost 2% of revenue.

Argyle Data’s revenue threat analytics applications quickly identify attack points and potential losses.

  • Fraud Threats - Identify threats from various types of domestic fraud and roaming fraud
  • Profit Threats - Identify threats from arbitrage, negative margin, high usage, and bill shock
  • SLA Threats - Identify threats from network vulnerabilities and from roaming partners not meeting their SLA windows
  • Forensic Threats - Graph analysis application for analyzing 1st to 5th degrees of separation between data assets

Key highlights

Lower business risks 

About Argyle Data
Argyle Data is used by the world’s leading mobile operators to detect the fraud, profit, and SLA threats that cost the industry $38 billion dollars per year. Argyle Data’s industry-leading native Hadoop application suite uses the latest Hadoop and machine learning technologies, proven at Facebook and Google, to identify the revenue threats and attack patterns being waged against mobile networks in real time. 


Using Hadoop to Drive Down Fraud for Telcos

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