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Fraud Detection through Unstructured Big Data Applications

In any line of business or government activity that involves a lot of money, fraud will be taking place. The financial, healthcare, insurance, retail, and social security sectors are particularly prone to fraud. Yet in a world where almost every aspect of life is documented, or in some way digitally recorded, the evidence is out there to enable organizations to drastically reduce the incidence of fraud.

As with many emerging Big Data applications, the most effective strategies combine the available structured and unstructured content, providing a holistic view. For example:

  • Leverage information from interview notes, email conversations and social media sites, and combine insight gained from those (unstructured) sources with official (structured) records and transactions
  • Compare trends, and detect unusual patterns of behaviour
  • Identify hidden relationships through network analysis and data correlation


KEY ISSUES
The foundation for effective fraud detection lies in:

  • Capturing, normalizing and enriching unstructured data sources, to prepare them for analysis
  • Creating a highly agile environment which enables fraud detection professionals to act on hunches, and test ideas

The technology to create game-changing fraud detection systems exists, and much of it is open source. The missing ingredients are initiative and expertise:

  • You know your business, and you probably have a range of ideas that you’d love to explore, perhaps beginning with a proof-of-concept system
  • We know the technology intimately, and can provide the design and implementation expertise to enable your concepts become business-transforming applications



Contact us for an informal discussion of your fraud detection ideas.