Countering Public Grant Fraud in Spain
Machine Learning for Assessing Risks and Targeting Control Activities
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.
Also available in: Spanish
Fraud in public grants: Piloting a data-driven risk model in Spain
This chapter presents a proof-of-concept for a risk model that the General Comptroller of the State Administration (Intervención General de la Administración del Estado, IGAE) of Spain can employ to assess fraud risks and detect likely fraud cases. The chapter presents an overview of the machine learning methodology that underlies the risk model, as well as a detailed account of how the model was built, based on data that are readily available to the IGAE. The chapter concludes with a discussion about the results of the model and recommendations for the IGAE to build on the proof-of-concept.
Also available in: Spanish
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