Transforming Financial Auditing in the Era of Artificial Intelligence: A Mixed Methods Study to Improve Efficiency and Accuracy

The rapid development of artificial intelligence (AI) presents new challenges and opportunities in the practice of financial auditing, especially regarding the efficiency and accuracy of audit results, which are still the main problems. This study aims to examine the impact of financial audit transformation through the application of AI using a mixed methods approach. Quantitative data were collected from 100-150 internal and external auditors in medium to large companies in East Java Province who have been using AI for at least one year, and analysed using Structural Equation Modeling (SEM) with AMOS. Qualitative data was obtained through in-depth interviews to explore perceptions and challenges of AI implementation. The results showed that AI significantly improved the efficiency of the audit process and the accuracy of risk and fraud detection, despite barriers such as change resistance and limited auditor competence. This research makes important contributions to the development of modern audit theory and offers strategic recommendations for practitioners and regulators to optimise AI integration in financial auditing. Practical implications include the need for auditor training and strengthening technology infrastructure to support sustainable digital transformation.