Operators are unable to detect complex new fraud because approaches currently used to detect fraud in communications networks typically rely on static rules with pre-set thresholds, and can only detect known fraud types,” according to Argyle Data.
Modern cyber-attacks evolve faster than an analyst can write rules to detect them. This type of mutating attack happens on a massive scale, siphoning off millions of dollars in just a few minutes, and is impervious to detection with traditional methods.
Rapid identification of suspicious traffic is the ultimate goal of telco fraud analysts but again, most existing fraud prevention technologies require time-consuming manual review resulting in costly delays in remediation.
“There is a growing need for new fraud detection solutions that can adapt to evolving network crime and usage patterns and all the signs point to machine learning as the answer,” said Mohan Gyani, member of the Argyle Data board of directors.
“Facebook, Google, and LinkedIn have pioneered big data and machine learning approaches to protecting their subscribers and gaining insight on vast amounts of data,” said Gyani. “The way in which Argyle Data is applying its supervised and unsupervised machine learning techniques to solve fraud in the mobile industry is highly innovative.”
Gyani said this was not possible until the advent of unsupervised machine learning, which makes it possible to analyze massive amounts of data in seconds and present instant alerts about anomalous traffic to fraud analysts.