Fraud in public grant programmes diverts taxpayers’ money away from essential services and reduces benefits for well-meaning recipients. When individual beneficiaries, private providers or government officials defraud grant programmes, they not only undermine the integrity of the programme itself, but they also risk eroding trust in government. In the wake of the COVID-19 pandemic, marked by a high volume of accelerated spending, fraud risks have become a pressing concern for governments worldwide.

Fraud is by nature a hidden activity, so how can authorities detect and mitigate risks effectively? This report identifies ways for Spain’s General Comptroller of the State Administration (Intervención General de la Administración del Estado, IGAE) to tackle this challenge, using state-of the-art machine learning models, and effectively target its control activities to the highest fraud risks found in public grants and subsidies.

This chapter provides an overview of the General Comptroller of the State Administration (Intervención General de la Administración del Estado, IGAE) and its oversight of public grants and subsidies in Spain. It describes the IGAE’s current approach to risk-based planning, and highlights preconditions and considerations for the IGAE to advance its use of grant data for assessing fraud risks. This includes considerations and recommendations for ensuring effective data governance and data management, as well as building capacity for using machine learning models.

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.

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.