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Making Data, Catching Thieves

In my work, I’ve found the following story pertinent recently…

“A bank identifies some fraudulent activity. After an investigation the police arrest the criminal. While they suspect the criminal had help from within – the “insider threat”, they can’t determine who. Months later, a bank employee provides a new address to the payroll department. This new address just happens to be the same as the address of the criminal on file in the closed investigation.”

You’d assume that in the following situation, most monitoring systems would pick up on this. However, you’d assume wrong - virtually no organisation in the world have systems that would be aware enough. Unless in the above example, someone thinks of searching the systems specifically for this scenario the potential co-conspirator will go unnoticed.

How do you solve this problem then? The solution lies in enabling organisations to detect data conditions that warrant additional analysis, whether that be automated or human - a notion of “data finds the data” or “relevance finds the user,” if you like. This is known as perpetual analytics, and yes, the company I work for does produce an example of this - IBM Relationship Resolution, which you want to, you can take a look at here (opens in new tab).

Leaving aside the question of the brand of technology, whether it is an organisation trying to address fraud or a country trying to mitigate threats – a perpetual analytics approach will help you root out risk by simply taking better advantage of information assets.

And what’s it will do so in real-time - in comparison to batch-based intelligence systems that rely on a “re-boil the ocean” principle. These systems are less intelligent and available enterprise insight is delayed until the next batch process is run - which won’t help us find our thief in the story.

Ultimately as this Netcrime blog points to, with increasingly widespread online fraud, how many days, weeks or months do you want to wait until you have the right answer? Because now, with perpetual analytics you don’t have to wait … it is now possible to “Know now!”

Postings on this site don’t necessarily represent IBM’s positions, strategies or opinions.

Jeff Jonas (opens in new tab) is the chief scientist of IBM Software Group’s Threat and Fraud Intelligence unit and works on technologies designed to maximize enterprise awareness. Jeff also spends a large chunk of his time working on privacy and civil liberty protections. He will be writing a series of guest posts for Netcrime Blog.

For more on Entity Analytics, click here.em> (opens in new tab)