Abstract: Continuous auditing is defined as a review of internal controls, transactions, or balances on an ongoing basis, with corresponding evidence creation, through the integration of traditional and emerging technologies. Within the context of modern AI advancements, continuous auditing must increasingly deliver the following principles: 1. Ongoing monitoring of controls and related risk indicators; 2. Sampling or testing of controls on a risk-based, continuous, and non-disruptive basis; 3. Continuous assurance of specific assertions; 4. Exploratory analysis, much of it driven by anomaly detection and classification; and 5. Control testing and assurance that are comparatively transparent and incorporate audit trail verification. These principles are intended to transcend simple automation of historic data analysis and offer insight to those responsible for governance.

The delivery of contemporary continuous audit disciplines is driven by responsible use of AI-powered technologies, especially in the domains of natural language processing, machine learning, and advanced robotics. The evolving possibilities around continuous auditing using AI to meet these principles are becoming possible in response to advances in business process automation and AI-enabled transaction processing; stakeholder expectations around ongoing assurance; the emergence of third-party assurance-as-a-service providing the leveraging of AI capabilities for assurance; the governance, risk management, and compliance focus on ongoing risk assurance and control testing; and increasing regulatory pressure for continuous audit frameworks in specific domains such as banking, treasury, procurement, and revenue assurance based on qualitative and quantitative management information.

Keywords: AI-enabled continuous auditing, Corporate governance analytics, Real-time audit monitoring, Intelligent risk assessment, Automated internal controls, Machine learning in auditing, Continuous compliance assurance, Audit data analytics, Predictive risk modeling, Governance, risk, and compliance (GRC) automation, Anomaly and fraud detection systems, Algorithmic audit assurance, Explainable AI for audit decisions, Continuous financial oversight, Technology-driven audit governance.

Downloads: | DOI: 10.17148/IJIREEICE.2024.121209