Navigating the Future of Auditing: AI Applications, Ethical Considerations, and Industry Perspectives on Big Data

Abstract

AI and Big Data make the traditional landscape of auditing change by further improving auditing practices in terms of efficiency, accuracy, and enhancement of risk management capabilities. The paper summarizes significant discussions on the application areas, benefits, challenges, and relevant ethics related to AI in extensive data auditing. It also considers crucial applications of AI in the audit realm, such as anomaly detection, predictive analytics, process automation, and real-time monitoring. Finally, it discusses various challenges, viz., the use of AI within the auditing domain: data security risks, biases, regulatory compliance, and ever-evolving requirements towards auditors' competencies. Conclusions are drawn on the industry-specific adoption trends showing how different industries, from finance to healthcare, retail, and manufacturing, have adopted AI. Furthermore, some sections address the issue of ethics: transparency, privacy, and fairness are discussed as high-priority issues to create trust in AI-augmented audits. The findings support that while AI and big data hold much potential in transforming auditing, successful deliverables on these are dependent upon how these technical and ethical challenges will be addressed and nurtured in interdisciplinary talent. The paper concludes with future trend insight that real-time auditing, predictive analytics, and blockchains for security in data management will have increased adoption. The studies further highlight that to drive full benefits from AI in auditing; there needs to be further innovation, ethical solid frameworks, and adaptive skill development to ensure integrity and accountability.

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