Multidimensional Audit Data Selection (MADS): A Framework for Using Data Analytics in the Audit Data Selection Process

TLDR

Advances in data analytics enable auditors to process the entire transaction population to flag outliers likely to be misstated, yet these techniques often yield so many outliers that full investigation is impractical. This study proposes a Multidimensional Audit Data Selection (MADS) framework that provides a systematic approach for auditors to use data analytics in the audit data selection process. The framework also addresses the common obstacle of handling a potentially large number of outliers when applying analytics to the entire population. By identifying problematic items from the entire population using analytics and then prioritizing them, the framework lets auditors focus on high‑risk items and thereby enhances audit effectiveness.

Abstract

SYNOPSIS Advances in data analytics techniques allow auditors to process the entire population of transaction data to identify outliers (i.e., unusual/suspicious transactions) that are more likely to be subject to misstatement. However, these techniques often generate a large number of outliers, making it impractical for auditors to investigate them in their entirety when performing substantive tests. This study proposes a Multidimensional Audit Data Selection (MADS) framework that provides a systematic approach for auditors to use data analytics in the audit data selection process. The framework also addresses a common obstacle of applying data analytics to the entire population of data—dealing with a potentially large number of outliers. By identifying problematic items from the entire population using data analytics and then applying prioritization methodologies to the resulting items, this framework allows auditors to focus on items with a higher risk of material misstatement and ultimately enhance the effectiveness of the audit.