Preventing Corruption in Procurement through Big Data

Indonesia, 23-27 November 2020 - Public procurement is estimated to account on average for 15-20% of GDP in developing countries and about a third of national government budgets, making it the largest single area of government spending. The size of the procurement market, its proportion as a percentage of GDP and the fact that procurement involves interaction between public and private sector, makes it vulnerable to corruption and a primary area of concern for the integrity of public administration. Estimates indicate that 20-25% of the procurement budget is drained through corruption (OECD, 2013). Preventing and combating corruption in public procurement is central to improving the efficiency and effectiveness of public services, and hence to achieve the 2030 Agenda for Sustainable Development.

Training slide on the stages of the data management, manipulation and analysis cycle

Advances in data analytics have opened new opportunities to revitalize efforts to prevent corruption through digitalization, the subject of a series of government and civil society initiatives to enhance procurement reform in the Indonesian context. To further integrate cutting edge analysis methods to fight corruption in procurement, UNODC held an online Training of Trainers (ToT) from 23 – 27 November 2020. Training topics included critical issues in procurement, procurement red flags and corruption risks, data formats and analysis tools, describing data and computing indicators, a study report by Indonesia’s Corruption Eradication Commission (KPK) on corruption in public procurement and KPK big data analysis on public procurement.

Screenshot of training on big data analysis on corruption in public procurement

A total of twelve participants attended the training, resulting in pre- and post-test averages of 55% to 73% respectively. In the evaluative feedback, participants shared recommendations to increase discussion sessions and interactive group work and to integrate more examples from other countries.

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