Auditing and Accountability
Summary
Auditing and accountability together form the backbone of trustworthy financial and operational reporting in both public and private organisations. Auditing refers to the independent examination of records, processes and controls to ensure that statements of performance and position are accurate, complete and free from material misstatement. Accountability is the obligation of managers and officers to justify their decisions and accept responsibility for outcomes. In recent years audit methodologies have evolved from periodic, sample-based reviews to continuous, risk-based approaches that leverage data analytics and artificial intelligence. Internal audit functions have broadened their remit beyond compliance, offering strategic insight on emerging risks, governance frameworks and ethical culture. External auditors, regulators and oversight bodies have stepped up inspections and reporting requirements to bolster transparency and stakeholder confidence. Across sectors, enhanced audit committee oversight, professional scepticism and technological innovation are redefining how organisations detect anomalies, prevent fraud and respond to public concerns. The global significance of these developments lies in their ability to underpin sustainable growth, protect investors and strengthen public trust in institutions.
Research from Nature Portfolio
Recent studies have explored the transformative potential of artificial intelligence within internal audit functions. A systematic review proposes the CACS framework—Commitment, Access, Capability and Skills development—to guide effective AI adoption in internal audit. This work highlights AI’s affordances for automating routine procedures, increasing strategic oversight and adding value through predictive risk assessments, while also recognising managerial and ethical constraints. The authors call for updated professional standards and regulatory guidance to ensure that internal auditors can assess AI-derived insights critically and maintain professional scepticism. By offering a structured research agenda, this contribution lays a theoretical foundation for integrating AI into internal audit workflows and informs regulators, audit practitioners and technology providers about best practices and emerging challenges.
Auditing and Accountability publication trend
The graph below shows the total number of articles in auditing and accountability across all publications each year (not limited to Nature Index journals).
Technical terms
Audit quality: The likelihood that an audit will detect and report material misstatements in financial statements.
Internal audit function: An independent department within an organisation tasked with evaluating and improving risk management, control and governance processes.
Risk-based auditing: An approach to audit planning that focuses resources on areas of greatest risk to organisational objectives.
Continuous monitoring: The use of automated analytics to review transactions, controls and performance metrics on an ongoing basis.
Professional scepticism: An auditor’s questioning mindset that critically assesses audit evidence and client assertions.
Artificial intelligence (AI): Computational techniques that learn patterns from data to perform tasks—such as anomaly detection or predictive modelling—without explicit programming for each scenario.
References
- Monitoring and audit quality: Does quality standards compliance matter?. Cogent Business & Management (2024).
- Can industry information disclosure improve audit quality?. China Journal of Accounting Research (2023).
- Artificial intelligence and the future of the internal audit function. Humanities and Social Sciences Communications (2024).
About these summaries
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