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Technology

eyeDES is a new technology that has been implemented in next-generation fraud management solutions, including automatic analysis and monitoring. The solutions can be customized according to the client database characteristics and requirements and  can be easily updated on a regular basis. The technology is based on a combination of artificial intelligence algorithms.

In essence, the technology provides the ability to develop and deploy multiple risk prediction scoring and decision making models spanning all types of fraud in industries like banking, insurance, and telecommunications. eyeDES solutions not only span and detect the whole spectrum of fraud types, but also support all fundamental role players, processes and procedures to reduce and manage fraud risk.

The performance of eyeDES solutions have been tested and results indicate that fraud detection rates rank as one of the highest and even exceed detection rates demonstrated by other available products on the market.
  		 

  • "You have to learn the rules of the game. And then you have to play better than anyone else."

     

    Albert Einstein

Latest Headlines

  • Reducing Losses from Reseller Fraud: A Merchants Perspective

    Online criminals have gone through a dramatic process improvement that’s hitting online merchants and direct marketers hard with a new level of reseller fraud and affiliate fraud.

    Online merchants offering affiliate programs, reseller programs and multi-payment trials are undergoing significant fraud increases resulting in higher charge backs, penalties and losses related to shipped-goods.

    Read more...

  • Fraud - including scams, online theft, insurance cheats and tax fraud - costs the UK £30bn a year, according to an official estimate.

    The National Fraud Authority (NFA) has calculated the figure for the first time and said it equated to £621 per adult in the UK.

    Read more...

Technology

  • Techniques used for fraud detection fall into two primary classes: statistical techniques and artificial intelligence (Palshikar 2002). Examples of statistical data analysis techniques are:

    • Data preprocessing techniques for detection, validation, error correction, and filling up of missing or incorrect data.

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