World Bank uses large data to help prevent corrosion

In March 2013, the World Bank held a seminar on "Large DC data Exploration" in Washington. At the symposium, about 150 data analysts from various fields shared and deepened the global challenges of how to use data analysis to address poverty and prevent corruption. The symposium also became a study and discussion of large data by the World Bank after governments in Europe and the United States and even the UN announced the push to open the data program.

One of the World Bank's efforts is to promote transparency and accountability on a global scale. "The World Bank's Open data program has grown rapidly". "For us, big data can help us integrate our data with data from other partners to help achieve the bank's development plan," said McDonough, vice president and chief accountant of the World Bank. ”

The World Bank's Open data plan is a joint project involving the World Bank and other agencies, including the United Nations Development Programme, the United Nations Business Development Agency, UN Global Pulse and the Qatar Institute of Computing. Comprised of 8 projects launched by the World Bank's open finance team and Datakind, a non-profit organization. The aim is to promote cooperation between social organizations in data and development.

Patrick Meier, director of the Social Innovation Division of the Qatar Institute of Computing, said: "There is a need for extensive cooperation between different organizations." Such cooperation is not easy, especially in the case of cooperation between different institutions. This is an iterative process. We are all stones. ”

"One of the major challenges to eradicating poverty is the risk of corruption," said Stephen Zimmermann, director of the World Bank's operations department. "Through this week's seminar, we've found a lot of innovative ways to use big data to prevent corruption," he said.

For example, in the use of food prices in Kenya, bananas and rice consumption can quickly analyze and forecast the local inflation rate. Thus providing support for more rational local assistance. In addition, Google offers an analytical tool for preventing corruption.

Coincidentally, a recent white paper published by KPMG also put forward the use of "proactive data analysis to ensure corporate anti-corruption compliance". According to the anti-corruption laws of the enterprises in Europe and America, the traditional enterprise self-check is the requirement of whether the enterprises check their own rules and regulations, and then carry on spot checks on some transactions. However, now that most businesses have stored their financial data, and with the popularity of large data analysis, corporate internal auditors can use data analysis tools and techniques to "spot" the high-risk transactions. But once the illegal act, the enterprise has the data analysis which is against the corruption to be able to help the enterprise to reduce some legal liability.

For example, through data analysis, enterprises can identify some of the high-risk types of transactions. such as those with government departments, the analysis of transactions involving large amounts of cash, or involved in bidding transactions and so on. Internal audits can be reviewed specifically for these projects.

For the World Bank's data-opening program, it is most important to share data among different organizations and data analysis based on data sharing. "It's a long process," as Datakind's director, Jake Porway, said:

"What is important is the sustainability of these plans. The data-opening plan should not just be a free practiced platform for data-analysis enthusiasts, it should be a link between data scientists and those who need to eradicate poverty and prevent corruption. Should play a realistic role. ”