Governance, Risk & Compliance

Governance, Risk & Compliance

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March 15, 2013

Mexican Operator AXTEL Continues to Combat Fraud



Mexican operator AXTEL has signed another contract to continue to develop its use of the Minotaur Fraud Management Solution (FMS) to combat fraud. The direct result of Minotaur’s success over the last nine years in reducing levels of fraud by over 85 percent has prompted the Mexican operator to renew the contract with Neural Technologies.

Over the years, AXTEL has extended the scope of the Minotaur solution from fraud management to also include risk management, resulting in outstanding benefits and savings for the operator, said the risk management solutions provider.

According to Neural Technologies, the award-winning Minotaur platform is the preferred solution for operators worldwide, due to its resilience, flexibility and intuitive GUI which allows users to make changes in-house for faster response times. It provides comprehensive protection against a multitude of fraud types, helping organizations detect fraud, whether the source is internal, external, customer, partner or dealer.  Per company’s explanation, Minotaur combines multiple approaches, including neural predictive analytics, behavioral modeling, link analysis and identity matching techniques.  It uses these, in conjunction with business rules, to profile customer, dealer, transactional and network activity to highlight any irregularities that could be indicative of fraud.

In a statement, Jorge de la Garza, Internal Collection Manager at AXTEL, stated, “Our Minotaur FMS allows us to adapt it to be as creative as we wish in order to achieve our objectives. We see Neural Technologies as a long-term partner in our strategic fraud plans.”Neural Technologies’ CEO, Ian Gebbett, commented, “We are delighted to extend our services with AXTEL after a very successful nine year relationship. We will continue to partner and work closely to ensure both speed of fraud detection and continued savings achieved.”




Edited by Brooke Neuman
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