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Reducing Audit Burden Through Multi-Period Monetary Unit Sampling
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Resumo(s)
Financial audits are one of the most powerful tools against corruption and in safeguarding
transparency. Typically conducted at the end of a reference period, these audits can be
extremely time-consuming and stressful for auditors. One commonly used method to perform
these audits is Monetary Unit Sampling (MUS), valued for its simplicity, as it does not require
detailed knowledge of the population and is available in most statistical software. However,
this simplicity comes at a cost, namely larger sample sizes and greater estimated sampling
errors. To address these limitations, the European Commission developed the MUS Standard
Approach, which is an improved version of the traditional MUS that incorporates more
information about the population, consequently mitigating some of the disadvantages of the
traditional MUS. This thesis explores the Multi-Period MUS, which is a variant of the MUS
Standard Approach that allows for the spreading of the audit effort throughout the reference
period. The ultimate goal is to demonstrate that the Multi-Period MUS is as safe as the MUS
Standard approach, and that it can be a great alias in reducing the burden and pressure on
auditors. Through Monte Carlo simulations and a pseudo-population of financial transactions,
the performance of each method was assessed based on the accuracy, precision, and bias of
the projected error estimator. The findings suggest that, although the Multi-Period MUS
exhibited greater variability in both the error and variance estimations, it delivered more
accurate results. This analysis supports the conclusion that the Multi-Period MUS is a robust
and effective approach for distributing the audit effort across multiple periods.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and Management
Palavras-chave
Monetary Unit Sampling MUS Standard Approach Multi-Period MUS financial audit audit burden projected error accuracy precision SDG 9 - Industry, innovation and infrastructure SDG 16 - Peace, justice and strong institutions SDG 17 - Partnerships for the goals