Artificial Intelligence in Auditing: Transforming Fraud Detection, Risk Assessment and Assurance Quality in Financial Reporting

Abstract

AI, in turn, bears its own set of consequences, markedly changing the course of work for auditors in terms of making it smoother, more precise, and less error-prone, with a reduced risk of fraud as its foundation. The traditional auditing process was systematized and prone to errors due to time and human factors. Improved access to better data analytics, anomaly detectors, and predictive modelling tools was also possible under AI, allowing auditors to undertake their work more effectively with large amounts of data. The current paper aims to address the role of AI in transforming auditing processes, emphasizing the augmentation of efficiency, risk mitigation, and the elimination of financial statement disclosure. The study employs a mixed-method search approach, integrating the latest financial reports and case studies as secondary data, in addition to a survey of 200 auditors from multinational companies being studied. The study has found that auditing-driven studies enhance real-time risk assessment, reduce operating costs, and ensure compliance in a global setting. Auditors further contended that they had increased trust in detecting aberrants as well as suppressing money. They have presented such barriers as algorithmic transparency, ethical dilemmas, and a lack of skills as having hampered full implementation, although not without standing. The research concludes that the report on the implementation of AI in auditing is neither a redirection of technology nor a strategic redirection, but rather involves a process of constant education, principles, and policy interventions. Further studies are therefore aimed at developing models that strike a balance between automation and understanding of human beings to promote further Accountability and trust within the limits of audit processes.