Data mining for red flags indicating corruption

Two sociologists have developed a method for finding red flags of corruption in public databases:

Researchers at the University of Cambridge has developed a series of algorithms than mine public procurement data for “red flags” — signs of the abuse of public finances. Scientists interviewed experts on public corruption to identify the kinds of anomalies that might indicate something fishy.

Their research allowed them to hone in on a series of red flags, like an unusually short tender period. If a request for proposal is issued by the government on a Friday and a contract is awarded on Monday — red flag…

Some of the other red flags identified by the researchers includes tender modifications that result in larger contracts, few bidders in a typically competitive industry, and inaccessible or unusually complex tender documents…

“Imagine a mobile app containing local CRI data, and a street that’s in bad need of repair. You can find out when public funds were allocated, who to, how the contract was awarded, how the company ranks for corruption,” explained Fazekas. “Then you can take a photo of the damaged street and add it to the database, tagging contracts and companies.”

This is a good use of data mining as it doesn’t require theoretical explanations after the fact. Why not make use of such public information?

At the same time, simply finding these red flags may not be enough. I could imagine websites that track all of these findings and dog officials and candidates. Yet, are these red flags proof of corruption or just indicative that more digging needs to be done? There could be situations where officials would justify these anomalies. It could still take persistent effort and media attention to push from just noting these anomalies to suggesting a response is required.

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