SOURCE: Argyle Data

Argyle Data

March 17, 2016 06:00 ET

Argyle Data Explains Why Credit Ratings Fail Against Growing Threat of Mobile 'Never Pay' Fraud

Losses From 'Never Pay' Contribute to 155% Rise in Subscription Fraud to $8.1 Billion in 2015

SAN MATEO, CA--(Marketwired - Mar 17, 2016) - 'Never Pay' is a new form of subscription fraud that has become a major challenge for mobile operators, contributing to revenue losses of $8.1 billion in 2015. Dr. Ian Howells, Chief Marketing Officer of Argyle Data, the leader in machine learning and real time data lake insights, discusses ways to address this challenge in the latest of a series of articles analyzing the results of the CFCA's 2015 Global Fraud Loss Survey1

In 2013 subscription fraud (use of service with no intent to pay) accounted for $5.22 billion or 11% of total losses worldwide. In 2015, the CFCA's 2015 survey recognized the seriousness of the problem by increasing the categories of subscription fraud: credit muling or the use of real identity details; application fraud where false details are created; and identity, where an owner's details are stolen. In total, the category has grown by 155% since 2013 and, at $11.2 billion, now accounts for 30% of overall revenue losses.

In the hyper-competitive mobile communications space, operators frequently run promotional offers whereby they buy out existing contracts and provide heavily subsidized devices. The 'never pay' or 'first default' scam involves obtaining a subsidized and, usually, post-paid mobile phone, and then failing to pay on the due date of the invoice. In a variation on 'never pay', the subscriber pays for a short while but either stops paying or does not pay the full amount, sometimes repeatedly. This is commonly called 'unable to pay.'

"SEC and equivalent financial filings by mobile communications providers show 'never pay' has evolved into a significant revenue drain," said Dr. Howells. "It is obvious that traditional checks such as consumer credit ratings and FICO scores are failing to stem the problem. The features of this revenue loss change over time, which adds to the complexity of preventing it. What is needed is a new way to determine whether, to whom and at what level operators should offer subsidized plans."

"'Never pay and its variants are the new attack methods of choice for criminal gangs," added Dr. Howells. "Traditional approaches are not coping with this new growth area of crime. The only way to halt the huge losses from sophisticated, evolving subscription fraud is by taking a 21st Century approach. The way forward is to use machine learning and anomaly detection applications to identify crime characteristics by accessing and ingesting silos of traditionally separated data into vast data lakes stored in Hadoop."

Further details of strategies for using big data, machine learning and data lakes to fight mobile fraud are contained in Argyle Data's ebook, 'Fighting Future Fraud,' which is available for download here.

About Argyle Data
Argyle Data is used by the world's leading mobile operators to detect fraud, profit, and SLA threats that cost the industry $38 billion per year. Argyle Data's industry-leading native Hadoop application suite uses the latest machine learning technologies against a unique, comprehensive data lake to give communications service providers a 360-degree view of user activities, allowing them to detect in real time the previously undiscoverable revenue threats and attack patterns being waged against their networks. To learn more please visit:
Argyle Data Website
Fraud & Technology Wire

1 Published 2015 by the Communications Fraud Control Association (CFCA)

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