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AI Case Study

The World Bank is exploring using machine learning to analyse and improve public spending processes and find signals indicating potential corruption with Microsoft Research

The World Bank is partnering with Microsoft Research to investigate using machine learning to analyse its public procurement and spending data, with the hopes of spotting corruption patterns and improve processes.


Public And Social Sector


Project Overview

" AI could help prevent and mitigate corruption risks as early as possible. This is part of a larger initiative that we are undertaking to help countries navigate how technology can positively transform the public sector.

Working with Microsoft’s Research group, we had the opportunity to see the power and potential of artificial intelligence to digest huge and diverse data sets to detect patterns that hint at the possibility of corrupt behavior. This would allow us to see links in bidding patterns of the winning and losing bidders to numeric patterns under “Benford’s Law,” along with beneficial ownership information from around the globe. It can also allow us to better map networks of relations, locations, use of shell companies, off-shore jurisdictions, and banking information of bidders to address potential risks before a contract is issued. In this early exploration of the potential of AI to improve public procurement, we’re collaborating with Microsoft, a leader in advanced artificial intelligence and machine learning to explore the potential of data driven corruption prediction and to identify data sets that prove to be of greatest value in detecting problems, and understanding what other types of data would be useful to probe."

Reported Results

Planned; results not yet available.






"The amount of goods and services that governments purchase to discharge their official business is a staggering $10 trillion per year – and is estimated at 10 to 25 percent of global GDP. Without effective public scrutiny, the risk of money being lost to corruption and misappropriation is vast. For its part, the World Bank has in place robust mechanisms to sanction firms who are found to have engaged in fraud and corruption in Bank-financed operations. But an ounce of prevention is always better than a pound of cure. As systems and procedures continue to become more digitized, there are more opportunities to leverage available data to find the red flags that can indicate corruption and other integrity risks."



"If we are able to collect and interrogate the available data we have on World Bank-financed procurement, and possibly combine it with datasets from other international organizations, national procurement data, and beneficial ownership or other corporate information, we can gain greater insight on how to make better decisions on public spending to assure greater value-for-money and mitigate the corrosive effects of corruption."

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