: machine learning for assessing risks and targeting control activities Organización de Cooperación y Desarrollo Económico creator text technical report Paris OECD Publishing 30 November 2021 2021 monographic eng pdf p. : gráf., tablas ; 1 documento PDF In the wake of the COVID-19 pandemic, governments face both old and new fraud risks, some at unprecedented levels, linked to spending on relief and recovery. Public grant programmes are a high-risk area, where any fraud ultimately diverts taxpayers’ money away from essential support for individuals and businesses. This report identifies how Spain’s General Comptroller of the State Administration (Intervención General de la Administración del Estado, IGAE) could better identify and control for grant fraud risks. It demonstrates how innovative machine learning techniques can support the IGAE in enhancing its assessment of fraud risks in grant data. It presents a working risk model, developed with datasets at the IGAE’s disposal, and maps datasets it could use in the future. The report also considers the preconditions for advanced analytics and risk assessments, including ways for the IGAE to improve its data governance and data management. specialized / OECD Todos los derechos reservados ; OECD Políticas públicas digitales España gobiernos fraude riesgos subvenciones públicas técnicas de aprendizaje automático gestión de datos Intervención General de la Administración del Estado pdf https://www.oecd.org/publications/countering-public-grant-fraud-in-spain-0ea22484-en.htm https://www.oecd.org/publications/countering-public-grant-fraud-in-spain-0ea22484-en.htm Todos los derechos reservados ; OECD ES-MaONT 211130 20211130141013.0 00006837