Countering public grant fraud in Spain
: machine learning for assessing risks and targeting control activitiesAutor(es):
Organización de Cooperación y Desarrollo Económico
Series OECD Public Governance ReviewsEditor: Paris : OECD Publishing, 30 November 2021Descripción: p. : gráf., tablas ; 1 documento PDFTipo de contenido: texto (visual) Tipo de medio: electrónico
Tipo de soporte: recurso en líneaSerie normalizada: OECD Public Governance Reviews
Tema(s): Políticas públicas digitales | España | gobiernos | fraude | riesgos | subvenciones públicas | técnicas de aprendizaje automático | gestión de datos | Intervención General de la Administración del EstadoRecursos en línea: 
Acceso al documento Resumen: 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.
| Tipo de ítem | Ubicación actual | Colección | Signatura | Estado | Notas | Fecha de vencimiento | Código de barras |
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Informes
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CDO
El Centro de Documentación del Observatorio Nacional de las Telecomunicaciones y de la Sociedad de la Información (CDO) os da la bienvenida al catálogo bibliográfico sobre recursos digitales en las materias de Tecnologías de la Información y telecomunicaciones, Servicios públicos digitales, Administración Electrónica y Economía digital.
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Colección digital | Acceso libre online | web | 1000020177031 |
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.
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