Can artificial intelligence stop corruption in its tracks?

By Vinay Sharma

AI and data have the potential to prevent corruption. Graphic: Nicholas Nam/World Bank

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. Citizens, rightly so, are demanding more transparency around the process for awarding government contracts. And, at the end of the day, corruption hurts the poor the most by reducing access to essential services such as health and education.

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. But how can human beings sift through vast amounts of data to connect the dots necessary to pick up on every red flag that hints at corruption — without slowing down the government procurement process and ultimately public service delivery?

This is why we’re looking at the promise of artificial intelligence (AI) to help us harness the power of technology to promote transparency in all aspects of government administration. 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. These are just a few examples of indicators that can be assessed to reveal potential concerns.

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. What’s even more exciting is the potential to take this exercise to scale and make this tool available to governments worldwide.

The promise is great, but much work remains to be done. 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. As we progress, we will be adding sets from other sources, expanding the pool of information to be probed, and further refining the tool’s potential to serve as a global public good.

Promoting the use of technology for more efficient, transparent, and responsive service delivery is a fundamental aspect of the World Bank’s commitment to fighting corruption.

Partnering with technology firms who are exploring a new frontier of possibilities will be critical to our success. This week at the Anti-Corruption Collective Action Conference, we will be sharing the initial findings of our work and hope to benefit from the input of leaders who are looking at revolutionary ways to stop corruption.

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