About the Project
Data Science for the Evaluation of Operational Errors and Their Impact
The project aims to develop new methodologies based on data science, including artificial intelligence, to estimate and predict the extent of errors and their impacts on operational programs supported by EU community funds in Portugal. The goal is to utilize these innovative methodologies as a replacement or complement to traditional audit practices, which rely solely on sample-based audits.
In Portugal, thousands of audit activities are conducted annually for funded operations, requiring significant time, human resources, and costs. Errors estimated at the program level have diverse implications, such as financial corrections (including extrapolated error corrections), the application of flat-rate corrections by the European Commission, and the need to develop improvement plans. Given the substantial impact of these errors on public policies and the significant financial volumes involved in these corrections, it is crucial to ensure the reliability of the estimates while simultaneously simplifying the effort required, especially in terms of human resources.
Traditional sample-based audits can sometimes yield inconclusive results, necessitating additional audit work. In recent years, the coordinator of this proposal has worked as an expert for the European Commission, developing innovative sampling methods—such as new standard monetary unit sampling, subsampling techniques, and multi-period sampling methods—which have significantly reduced audit efforts without compromising the confidence levels in error estimates. Despite these advancements, further innovation is needed to address the growing complexities of operational audits.
