(2004), "Patterns established", International Journal of Productivity and Performance Management, Vol. 53 No. 7. https://doi.org/10.1108/ijppm.2004.07953gab.001Download as .RIS
Emerald Group Publishing Limited
Copyright © 2004, Emerald Group Publishing Limited
Azure is implementing real-time “call-fingerprinting” technology to complement its existing rules-based and AI (artificial intelligence) fraud-detection engines, allowing telecom operators to identify new as well as conventional fraud patterns simultaneously. Call fingerprinting feeds directly into the fraud case-building process adding to data that is already interpreted and integrated to provide operators with fraud alarms.
Call fingerprinting allows for the identification of individuals or communities who have previously been identified as being of interest for investigation and monitoring. It enables the identification of fraudsters who may have changed their identities, because even though their identity might have changed, their communication habits may not.
Call fingerprinting will be of particular benefit to mobile operators, especially those in the pre-paid arena. They will now be able to detect recurring customers on new pre-pay mobiles. Normally they would be identified as new customers, rather than just existing customers on new phones taking advantage of new contract offers. By identifying communication behaviour, including traditional calls, SMS and other services, operators can create profiles to verify whether any fraudulent activity is taking place.
Pre-paid mobile customer churn can also be reduced as operators can track and manage customers without knowing who they are. By monitoring the events, identifying patterns and applying business intelligence this knowledge can also be applied as part of a customer service programme to retain users.
For more, see www.azuresolutions.com