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|Neural Technologies partners with Revector|
|Tuesday, 16 February 2016|
Risk management and analytics expert, Neural Technologies today announced a partnership with mobile anti-fraud and revenue protection specialist Revector. The alliance will offer communications service providers a comprehensive anti-fraud and revenue protection service to mitigate risk and eliminate revenue loss.
Neural and Rejector will combine their comprehensive risk management capabilities and award-winning products to offer the most comprehensive and real time anti-fraud and risk management service on the market.
The companies’ combined customer bases exceed 200 mobile operators globally and the partnership will enable customers to detect and locate fraud on their networks more effectively, as well as reduce revenue lost to billing errors.
Andy Gent, CEO of Revector, commented: “Fraud and revenue loss is a serious business for network operators and their shareholders. It also funds organised crime and limits the economic benefits that an effective communications network can deliver. Our partnership with Neural Technologies, which combines two multi-award winning revenue protection companies, will provide industry leading solutions to monitor, manage, detect and eliminate fraud and revenue loss for telecommunications companies.”
Luke Taylor, CCO of Neural Technologies, commented: “Our recent survey of telecommunications’ operators identified over-the-top (OTT) bypass as an increasing trend globally, affecting both revenues and customer trust. The companies are a natural fit - Revector has innovative solutions which, when combined with our expertise in analytics and risk management, can quickly eliminate the threat of fraud and we are pleased to be working with them.”
The two companies will be working together to provide:
• Detection and elimination of OTT bypass on mobile and fixed line networks
• A combined SIM box detection and elimination service
• Network level and local on-ground test event generation to ensure operators are billing accurately
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