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Optimized Audit with Risk-based Sampling [poster]
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Resumo(s)
Auditors aim to detect and quantify errors in financial statements, but the large volume of data makes full review impractical. Sampling is used instead, often through Monetary Unit Sampling (MUS), where items are selected with probability proportional to their value, reflecting that high-value items are more likely to carry significant error. Audit populations typically contain many items with no error and a small subset with highly variable errors [1]. Estimating total error becomes difficult when these non-zero errors are rare. Incorporating auxiliary information, such as historical risk assessments, can improve sampling precision [2]. Prior work has used such data to assess risk or refine error bounds [3, 4], but less attention has been given to using it at the sample selection stage. This study proposes a sampling method that integrates historical audit data to assign risk scores, allowing for risk-weighted selection. The goal is to better represent the error structure of the population and improve the accuracy of error estimates.
Descrição
Dias, I. (2025). Optimized Audit with Risk-based Sampling [poster]. Poster session presented at Data Research Meetup by MagIC, Lisboa, Portugal. --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.
Palavras-chave
SDG 9 - Industry, Innovation, and Infrastructure SDG 16 - Peace, Justice and Strong Institutions