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
Looking ahead: A roadmap of datasets to enhance the fraud risk model of Spain’s Comptroller General
This chapter explores additional datasets that the General Comptroller of the State Administration (Intervención General de la Administración del Estado, IGAE) of Spain can use to enhance the risk model described in Chapter 2. The chapter provides a road map and indicates which databases are most promising for improving the assessment of grant fraud risks using the model, based on the accessibility, relevance and quality of the datasets. The datasets are grouped into three categories: 1) organisational data on the parties of the granting process; 2) data on personal connections and conflicts of interest; and 3) data on organisational reliability and violation of rules.
Also available in: Spanish
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