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Subscription Fraud is different on every network, as some are more disposed for pre-paying or post-paying connections. Blacklists of ‘Hot addresses’ are typically maintained but far from complete, and can’t identify spoofed street names with no other customers
Example: A Fraudster will seek to gain a new SIM connection and/or handset by using forged or stolen identity + credit card + address or ‘account takeover’.
Traditional detection provides alerts generated in-store based on customer history or credit checks, or against some known hot addresses or names but doesn’t protect from stolen identity or address without previously known fraud. Alerts on high usage over time are too late to prevent handset loss.
Fraud never stops, it only changes. Fraudsters often change the spelling of names and address, translate to another language, or type letters instead of numbers.
A model is built for subscription fraud using connection data to ‘learn’ what subscription fraud looks like on each unique network. Machine Learning ‘scores’ each future connection against the known profile using proprietary ‘features’. This allows fraudulent connections to be identified, while also identifying new fraud approaches such as spoofed address with no genuine customers
Existing system: 120k subscribers connected through an online portal of a Tier 1 Operator in Europe for one year.
ROI estimated by Mavenir to be up to 2.5 M Euros (based on handset cost /unpaid services)