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Accurate Customized Solutions for Fraud Management

IDES Technologies introduces eyeDES, a next-generation, customized analytical technology for accurate and efficient enterprise fraud management solutions.

Our vision is to prevent fraud worldwide.

Our mission is to provide accurate and efficient enterprise fraud management solutions.

  • In essence, our technology provides the ability to develop and deploy multiple risk prediction scoring and decision making models covering many areas like:
    • card fraud
    • anti-money laundering
    • insurance fraud
    • telecommunications fraud
    • mobile phone fraud
  • The accurately designed models can be deployed in real time to create customized solutions that execute the created models on new, not previously seen data.
  • eyeDES solutions have shown high detection rates for both fraud and non fraud cases, as well as low false versus positive ratios.

eyeDES customized solutions, updated on a regular basis, are proven to be accurate, flexible, scalable and cost effective.
  		 

  • "Every great and deep difficulty bears in itself its own solution. It forces us to change our thinking in order to find it."

    Niels Bohr

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.

    Read more...