Designing a Model for Integrating Process Mining into Financial Statement Auditing

Abstract

The objective of the present study was to design a model for integrating process mining into the auditing of financial statements. The research employed an exploratory mixed-method approach (qualitative–quantitative). Participants in the qualitative phase included experts and specialists in financial statement auditing with experience in management and policy-making. Using purposive sampling and based on the principle of theoretical saturation, a sample size of 10 individuals was determined. Participants in the quantitative phase consisted of certified public accountants responsible for financial statement auditing in reporting companies in Tehran Province. Using stratified random sampling and Cochran’s formula, a sample size of 303 individuals was established. The data collection tools included semi-structured interviews for the qualitative phase and a researcher-made questionnaire for the quantitative phase. The qualitative data were analyzed using thematic analysis, and the quantitative data were analyzed through structural equation modeling with the use of SPSS and LISREL software. The findings indicated that 82% of the model design for integrating process mining into financial statement auditing depended on dimensions comprising four main themes: (1) individual factors, (2) environmental factors, (3) institutional factors, and (4) organizational factors; and four sub-themes: (1) adoption, (2) development of process mining in auditing, (3) implementation of process mining in auditing, and (4) performance evaluation of process mining in auditing. The results also demonstrated that the implementation of process mining enhances the reliability of audit results and, by replacing traditional manual auditing methods, strengthens the evidentiary power of audits. As a novel data mining technique, process mining equips auditors with tools to keep pace with technological advancements and challenges.