Von dem Buch Public Governance Reviews Countering Public Grant Fraud in Spain Machine Learning for Assessing Risks and Targeting Control Activities haben wir 2 gleiche oder sehr ähnliche Ausgaben identifiziert!
Falls Sie nur an einem bestimmten Exempar interessiert sind, können Sie aus der folgenden Liste jenes wählen, an dem Sie interessiert sind:
100%: OECD: Public Governance Reviews Countering Public Grant Fraud in Spain Machine Learning for Assessing Risks and Targeting Control Activities (ISBN: 9789264554368) OECD Publishing, in Englisch, auch als eBook.Nur diese Ausgabe anzeigen…
100%: OECD: Public Governance Reviews Countering Public Grant Fraud in Spain Machine Learning for Assessing Risks and Targeting Control Activities (ISBN: 9789264374669) OECD Publishing, in Englisch, auch als eBook.Nur diese Ausgabe anzeigen…
Public Governance Reviews Countering Public Grant Fraud in Spain Machine Learning for Assessing Risks and Targeting Control Activities
2 Angebote vergleichen
Bester Preis: € 16,27 (vom 01.11.2022)1
Public Governance Reviews Countering Public Grant Fraud in Spain Machine Learning for Assessing Risks and Targeting Control Activities
EN NW EB DLISBN: 9789264554368 bzw. 926455436X, in Englisch, OECD Publishing, neu, E-Book, elektronischer Download.
Lieferung aus: Vereinigtes Königreich Großbritannien und Nordirland, Despatched same working day before 3pm.
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 (Intervencion General de la Administracion 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.
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 (Intervencion General de la Administracion 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.
2
Public Governance Reviews Countering Public Grant Fraud in Spain Machine Learning for Assessing Risks and Targeting Control Activities
EN NW EB DLISBN: 9789264374669 bzw. 9264374663, in Englisch, OECD Publishing, neu, E-Book, elektronischer Download.
Lieferung aus: Vereinigtes Königreich Großbritannien und Nordirland, Despatched same working day before 3pm.
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 (Intervencion General de la Administracion 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.
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 (Intervencion General de la Administracion 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.
Lade…