Harnessing big data to uncover corruption in the forestry sector

Politically exposed persons (PEPs) (individuals entrusted with a prominent public function) may award contracts and permissions to entities in exchange for private gain, such as bribes to themselves or via corporate vehicles or contacts from which they derive beneficial ownership or other advantageous control. Big data, including publicly accessible open data, has strong potential to identify such corrupt actors. With the right methodology, this could be done affordably by conservation organizations to help financial institutions and financial investigation units freeze illicit assets and ultimately prosecute corruption in the forestry sector. New research undertaken by data analysts and financial flows experts at TRAFFIC and the Basel Institute on Governance under the Targeting Natural Resource Corruption project is testing new methods for automating data collection and analysis techniques used in other sectors. The hope is to identify potential corruption at the point of allocation of forest access rights. Join us to learn more about this exciting methodology, and for reflections from financial and enforcement experts. Learning questions 1. What role do politically exposed persons play in forestry corruption and how could these theoretically be detected? 2. What software tools exist for civil society to collect, analyze and enrich data to detect the risk of corruption? 3. What is the importance of regional data sets, native language skills and country-specific details such as naming conventions to this type of analysis? 4. How do we drive anti-corruption activity resulting from this analysis? What are the risks and opportunities that conservation organizations must keep in mind when sharing data with other stakeholders in the anti-corruption/financial crime arena?