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The Telecom industry faces a massive challenge: $46 billion is lost each year to sophisticated fraud attacks including call-back, international revenue sharing fraud, subscription fraud, etc.
We will discuss the use of unsupervised nonparametric methods for anomaly detection based on multivariate similarity. Such algorithms can be extremely effective in identifying changing fraud patterns (in the absence of labeled data),
as they are not trained on historical fraud cases but rather monitor “normal” subscriber behavior.
When labeled datasets are more commonly available, fraud departments often face a near-insurmountable imbalanced-class problem, as by definition fraud happens rarely and often goes undetected.
The talk will also address some recent supervised learning techniques that can deal with large imbalanced-class approaches for near-real-time identification of Telecom fraud.
Lastly, we will cover the production architecture of a machine learning system that can cope with the vast volumes of data flowing over carrier networks, to provide near-real-time prediction and minimize losses due to fraud.
Arshak is a machine learning focused product manager. He founded Fellowship.AI applied machine learning fellowship program and is a cofounder of Platform.AI.
He has delivered AI solutions for some of the largest enterprises in the world and multi-billion dollar quantitative hedge funds.
Previously Arshak served as the Chief Technology Officer at Sentient Technologies. He has also been in technology leadership roles at Argyle Data, Alpine, Endeca/Oracle.
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