SAN MATEO, Calif., Oct. 24, 2017 (GLOBE NEWSWIRE) — Argyle Data has demonstrated the ground-breaking capabilities of its machine learning-based predictive analytics for two European wholesale carriers in U.S. and European lab tests. 

According to recent reports by industry analysis firm ABI Research, machine learning-based predictive analytics are applicable to all aspects of the telecom business, and these technologies will lead operators to profoundly change how they manage the telecom business.  ABI estimates that mobile operators will devote more than U.S.$50 billion to big data and machine learning through 2021. 

Two-thirds of all carrier fraud losses are related to international traffic, and much of this traffic passes through wholesale networks. 

Mobile broadband operators worldwide have made major investments to develop internal fraud detection and prevention systems. However, the wide variations in technology, internal processes and national regulations make it difficult for operators to orchestrate fraud defenses beyond their own systems. 

For wholesale carriers, the task is exponentially tougher. Wholesalers typically receive only minimal data about their retail telecoms customers’ traffic – too little for rules and thresholds-based detection systems and too late for any action other than historical analysis.

In an increasingly competitive and tight-margin industry, wholesale carriers are seeking new ways to provide added value to their retail customers and affiliates. With international scams the highest contributors to mobile fraud losses, wholesalers are increasingly looking to predictive analytics to stop fraud as it occurs, regardless of the origin or destination of the suspect traffic.

1. Top tier French operator
One of the largest mobile/internet services operators in France and Africa with 265 million users worldwide was aware of largescale International Revenue Share Fraud cases leveraging their wholesale telecom network.  Their rules-and-thresholds-based fraud detection approach made it impossible to detect fraud in real time.  In fact, their existing system took up to six months to detect Subscription Fraud. 

The operator provided two weeks of historical data for their proof of concept trial in Argyle Data’s U.S. laboratories. A Supervised Machine Learning algorithm approach was used, leveraging a Hadoop architecture to create a data lake that eliminated the isolation of data into silos.

Even with this limited data, Argyle Data:

  • Identified multiple fraud types:  IRSF, Wangiri (call back), SIMbox, CLI spoofing and CLI absent
  • Uncovered an international fraud ring collaborating from three separate countries, none of which was the operator’s home country
  • Conclusively demonstrated that, despite the random and widely different traffic types routing through the wholesaler, Argyle Data’s machine learning application could establish a baseline norm from which anomalous traffic could be identified.

2.  European Communications Hub Provider
This European wholesaler provides an ecosystem for the creation and consumption of global communications in the areas of IP & Data, Cloud & Data Center, Corporate, Mobile and Voice.

One month of very limited historical data was provided.  This was analyzed in the local country laboratories for reasons of data security.  Again, a Supervised Machine Learning algorithm approach was used, based on a Hadoop data lake architecture.  Even with this severely restricted data, results included:

  • Identified high-cost /high intensity fraud that had been missed by the internal fraud system.
  • Generated almost double the number of alerts as the existing rules-based system for multiple fraud types including IRSF, Wangiri (call back), SIMbox, CLI spoofing and CLI absent
  • Confirmed machine learning’s detection capabilities to provide in-depth results with only minimal data 

Argyle Data VP of Engineering Padraig Stapleton said, “Initially, each of the wholesalers doubted whether a machine learning approach could be applied to wholesale traffic routing, because they were all too aware of the hugely varied amount of traffic crossing their networks and large gaps in traffic flow from different customers at different times.  The fear was that the machine learning system could not learn what was normal in order to create a baseline for detecting abnormalities.  Both operators were totally convinced after seeing our results, and our work with them is ongoing.  It’s hard to argue with an unprecedented achievement in fraud detection at the wholesale level.”

More information on these case studies is available at www.argyledata.com/resources.  

About Argyle Data™              
Argyle Data provides mobile carriers with machine learning analytics tools for high volume, high velocity data, allowing them to profile, detect, predict and act upon fraud, security or credit issues and address deteriorating subscriber behaviors that financially impact their business.   More information is available at www.argyledata.com.

 

CONTACT: Contact:
Mary McEvoy Carroll
Argyle Data
[email protected]
+ 1-408-691-4283