Analyzing the Impact of Adopting Data Analytics on Internal Auditing Efficiency: A Perspective from a Sample of Auditors in Algeria

Abstract

This study examines the impact of data analytics adoption on the efficiency of internal auditing, with a specific focus on a sample of auditors in Algeria. The research aims to address a notable gap in the existing literature by exploring the challenges and opportunities unique to the Algerian auditing environment. Using a quantitative approach, the study employs factor analysis and structural equation modeling to analyze responses from auditors in Algeria. The primary research question investigates the relationship between the application of data analytics and the efficiency of internal auditing. Key findings indicate a positive and statistically significant correlation between the two variables. Additionally, the study reveals that specific data analysis tools, such as Machine Learning algorithms, play a substantial role in enhancing audit efficiency. Furthermore, training programs significantly contribute to auditors' proficiency in utilizing data analytics tools. These findings provide practical implications for organizations and policymakers, emphasizing the strategic incorporation of data analytics and continuous training to optimize internal auditing processes in the Algerian context.