Artificial Intelligence in Auditing: Opportunities, Challenges, and the MindBridge Case

This thesis aims to analyze how Artificial Intelligence (AI) is transforming auditing procedures and redefining the role of auditors in an ever more digital environment. After a brief overview of AI’s growing role in professional sectors, particularly in auditing, it focuses on key AI technologies: machine learning and natural language processing (NLP). These tools are reshaping auditing practices through automation, predictive analysis and anomaly detention. The research compares traditional auditing, often challenged by big data volume, long-time spending procedures and human errors, with the digital AI based approach characterized by efficiency and time-saving. A case study on the MindBridge AI Auditor illustrates how statistical analysis, rule-based logic and machine based algorithms can enhance efficiency, accuracy and objectivity by facilitating auditing processes. MindBridge’s AI-powered financial risk intelligence platform revolutionizes audit risk assessment by leveraging data-driven risk scores to calibrate inherent risk evaluations.The case study highlights also the limitations related to dependence on data quality and the importance of human judgment. In this context, the thesis examines both strengths and weaknesses introduced by AI’s use in the auditing sector so far. The conclusion reflects future scenarios considering both technological competences and ethical awareness to fully leverage AI’s potential in auditing.