Recent developments in audit sampling techniques : a Maltese analysis

Abstract

PURPOSE: This study aims (i) to determine the current audit sampling techniques employed by Maltese audit firms, including the extent to which technology is currently utilised in determining sample size and selection, and (ii) to assess the potential application of Big Data Analytics (BDA) to enhance the efficiency and reliability of audit sampling. It also seeks to explore how such advancements can shape the future of audit methodologies. DESIGN: A qualitative approach was adopted for this study. Open-ended interviews were conducted with 10 research participants, comprising of 8 auditors and 2 data scientists. FINDINGS: The findings indicate that Maltese audit firms continue to rely on traditional audit sampling techniques. However, there is a noticeable shift as these conventional methods are increasingly complemented by technology. While most firms use Computer-Assisted Audit Techniques or other Excel-based tools, a growing number have begun to gradually integrate BDA. CONCLUSIONS: The study confirms that although traditional methods form the foundation, the incorporation of BDA mining tools is beginning to reshape these practices by enabling real-time analysis, full population testing, and enhanced fraud detection. Thus, a hybrid approach is currently most suitable to improve both efficiency and reliability of the sampling process. While the full potential of BDA has yet to be realised, particularly in smaller firms, audit firms are already exploring BDA through pilot or phased projects. IMPLICATIONS: This study hopes to clarify how integrating BDA into traditional audit sampling can enhance the audit sampling process. It also seeks to inform audit professionals on the strategic implications of adopting BDA, including the required investments, while helps manage stakeholder expectations by clarifying the realistic capabilities of BDA tools.