Chapter 10: AI-Enabled Auditing: Redefining Risk Detection, Fraud Prevention, and Predictive Intelligence

Integrating artificial intelligence (AI) tools in accounting and auditing is redefining conventional financial practices by leveraging machine learning (ML), natural language processing (NLP), and automating compliance that enhance fraud detection and enhance accuracy in risk assessment (Brown et al., 2012; Vasarhelyi et al., 2015). AI enables full population data analysis, allowing auditors to find potential fraud and irregularities in real time (Zhang et al., 2020). These developments contribute to higher efficiency, minimizing human errors and enhancing regulatory compliance. Predictive analytics driven by AI reshapes decision-making in risk management, resource allocation, and financial performance forecasting. By analyzing large volumes of structured and unstructured data, AI uncovers patterns that support strategic planning and strengthen financial resilience (McAfee & Brynjolfsson, 2017). Consequently, accountants are evolving from traditional advisory roles to strategic advisory positions. However, challenges such as data privacy, regulatory uncertainties, and ethical issues like algorithmic bias remain significant (DataRobot, 2022; Richins et al., 2017). Balancing AI efficiency with professional judgment is essential, emphasizing the need for continuous training to ensure transparency, fairness, and trust in financial reporting. It aims to provide a comprehensive understanding of how AI influences financial decisions, audit automation, and fraud prevention while exploring how auditors and accountants can adapt to technological transformation without compromising integrity and trust in financial reporting.