Saudi Cultural Missions Theses & Dissertations
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Item Restricted The role and use of Artificial Intelligence (Al tools) in audits of financial statements(Aston university, 2024-09) Alsaedi, Amal; George ,SalijenIntegrating artificial intelligence (AI) in the auditing function holds significant potential to transform the industry. As firms and stakeholders increasingly recognise the value of and demand audit quality, the accuracy, validity, and integrity of information generated by audit processes have become a vital consideration. Integrating AI into audit processes would be viewed as advancing audit techniques. However, the current limited adoption of this technology by audit firms raises concerns about their awareness of its transformative potential. This study aims to identify AI tools used in auditing and their impact on the audit process and quality. The study bridges the existing gap using a secondary exploratory method. Qualitative data was collected from transparency reports by the Big Four audit firms, i.e., KPMG, Deloitte, EY and PwC, and audit quality inspection reports for the four firms by FRC. For recency purposes, only reports published between 2020 and 2023 were considered. A thematic analysis of the data collected reveals that adoption of AI and data analytics in auditing is still low, and the Big Four firms are actively promoting increased adoption. The results demonstrate a notable disparity between potential and current applications, as shown by a clear gap between the publicised potential of AI and data analytics and their implementation within audit processes.38 0Item Restricted Applying Benford's Law to publicly traded companies in the United States from the year 2013 to 2022(Saudi Digital Library, 2023-09-01) Alanazi, Aeshsh Dhabyan; Wood, AnthonyFinancial fraud in financial statements is a significant concern that could have a negative impact on stakeholders such as lenders and shareholders. Benford's Law, a principle that describes the expected distribution of first digits in naturally occurring data, provides a path for detecting these fraud cases. By leveraging these anticipated patterns, analysts can uncover manipulated information, such as fabricated financial records. The study used two data sets to investigate this concept: one encompassing businesses with no allegations of financial deception and another encompassing those accused of such misconduct. The dataset included 105 records of business performance from different years obtained from an instructor and 35 cases of reported fraudulent activities within US firms. The researcher observed the financial data for adherence to Benford's Law using various statistical methods, including Mean Absolute Deviation (MAD), Kolmogorov-Smirnov (KS), and Chi square analyses. The findings show that, when compared to their legitimate counterparts, companies engaged in fraud are more likely to deviate from the expectations set by Benford's Law. This observation suggests that Benford's Law may be useful in detecting financial statement fraud. This study concludes that Benford's Law is useful for detecting financial statement fraud. However, when using Benford's Law to detect fraud, it is critical to account for additional variables. Along with applying Benford's Law, analysts should consider factors such as the company's size, volatility, and growth trajectory.28 0