A Review of Literature to Derive Best Practices for Big-Data Audits in Saudi Context
DOI:
https://doi.org/10.59992/IJFAES.2024.v3n10p1الكلمات المفتاحية:
Auditing، Big Data، Saudi Arabia، Literature Review، Best Practicesالملخص
In 2014, leading scholars in the field of big data auditing outlined a research agenda to explore these impacts, setting the foundation for further investigation. This paper explores the impact of big data on audit evidence, particularly in the context of Saudi Arabia’s transition to a more diversified economy under Vision 2030. This paper also seeks to synthesize current research and establish best practices for auditors in Saudi Arabia, emphasizing the importance of adapting audit processes to a growing economy that increasingly generates vast amounts of data. In recent decades, auditing practices have undergone substantial transformation by broadening the scope of audit goals and incorporating diverse, non-traditional sources of evidence, such as unstructured data from social media or IoT devices. These new data sources, while increasing audit accuracy and depth, pose challenges regarding data reliability and integrity. Over the past decade, many of these research questions have been addressed, particularly in relation to how big data enhances auditors' abilities to detect fraud, improve audit quality, and provide more accurate risk assessments.
By implementing AI algorithms and advanced analytics, auditors can gain deeper insights while also addressing key concerns such as data veracity, compliance with International Financial Reporting Standards (IFRS), and the guidelines set by the Saudi Organization for Certified Public Accountants (SOCPA). As Saudi Arabia's economy diversifies and becomes more data-driven, understanding and integrating big data into audit practices will be essential for maintaining high audit standards and supporting economic growth.
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