Applications of Artificial Intelligence in Auditing

The purpose of this research is to examine the application of artificial intelligence in auditing.

This study is applied in objective and descriptive-survey in terms of data collection, which was conducted with a qualitative approach. The population of this study consisted of experts and opinion leaders in the field of accounting and auditing, university professors and lecturers, and the board of directors of the national auditing organization, from which 24 individuals were selected using a purposive sampling method. Semi-structured interviews were utilized for data collection until theoretical saturation was achieved. For data analysis, systematic grounded theory and for analyses, three types of coding: open, axial, and selective, based on Strauss and Corbin's coding technique, were used.

The findings indicate that the model of artificial intelligence application in auditing includes causal conditions (human resources, financial resources, empowerment development, and competitiveness), contextual factors (accessibility, standardization, incentivization), strategies (strategy formulation and planning), and intervening conditions (management, cultural, infrastructure) outcomes (efficiency, accuracy, and speed).

Considering the research findings, it is necessary to develop the prerequisites of artificial intelligence in a process-oriented manner to create the context and motivation among auditors for the development of artificial intelligence applications in auditing in the country.