A Qualitative Study on the Use of Data Analytics in Auditing
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Abstract
This qualitative study investigates the utilization of data analytics in auditing, aiming to provide insights into its applications, benefits, challenges, and implications. Through a systematic literature review, the research explores the multifaceted aspects of data analytics integration in auditing practices. The research design encompasses a systematic review methodology, involving the identification, selection, and synthesis of relevant studies from academic databases and scholarly sources. Thematic analysis is employed to analyze the selected literature and identify key themes, patterns, and relationships. The findings reveal a wide range of applications of data analytics in auditing, including anomaly detection, predictive modeling, and text mining. Additionally, the study identifies several benefits associated with the use of data analytics, such as improved audit quality, enhanced risk detection capabilities, and greater efficiency in audit processes. However, the integration of data analytics also presents challenges, including data quality issues, technological limitations, skill gaps among auditors, and ethical considerations. Addressing these challenges requires investments in technology infrastructure, training programs, and organizational culture conducive to data-driven decision-making. The research contributes to the existing body of knowledge by offering valuable insights and recommendations for audit practitioners, policymakers, and educators
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