Artificial intelligence is changing how audits are performed, whether your organisation is using AI or not. As auditors adopt data analytics and AI-enabled tools, they can review larger volumes of transactions, identify unusual patterns more quickly and test information at a deeper level than traditional sampling methods allowed. This means expectations around data quality, documentation, governance and reporting are increasing.
For finance leaders and audit committees, the question is no longer whether AI will affect audit. It is whether your organisation’s reporting processes, controls and governance frameworks are ready for a more data-driven audit environment. Organisations with reliable data and strong oversight are likely to be well placed. Those relying on inconsistent information or undocumented processes may find weaknesses are harder to hide and easier to identify.
This focus on governance aligns with New Zealand’s AI Strategy and Responsible AI Guidance released by MBIE in July 2025, which emphasises accountability, oversight and responsible use of AI across organisations.
How Is AI Being Used in Audits Today?
Internal audit and assurance functions globally are increasingly adopting AI and advanced analytics to analyse larger datasets and improve risk assessment. It is a tool that supports auditors in:
- Data Analytics: AI has allowed auditors to more efficiently and effectively process large amounts of data. Traditionally, auditors sampled a portion of transactions to assess financial records. AI has enabled auditors to increase their dataset sizes leading to broader insights across larger populations of transactions.
- Exception Detection: These unusual items are often referred to as exceptions because they differ from expected patterns or established controls. AI will quickly identify and detect unusual transactions, errors or areas of concern from the entire dataset. Auditors are no longer required to go through their datasets manually and can instead focus their expertise on the areas with the highest risks.
- Pattern Recognition: AI tools can learn to recognise patterns in large datasets and highlight transactions that differ from normal behaviour. This improves risk assessment through stressing these anomalies, specifically those more difficult to detect, for auditors to review.
- Continuous Monitoring: AI also enables more frequent monitoring and reviews of information. Compared to traditional methods limiting auditors to periodic reviews, AI allows auditors to provide timely insights and earlier risk intervention.
Importantly, professional judgement is still required. Auditors still interpret findings, assess context, challenge assumptions and determine whether further work is required. AI is best understood as another tool within the audit process, helping auditors review more information and focus their expertise where it matters most.
What Changes for Finance Teams?
Why Data Quality Matters More Than Ever
For most finance teams, the bigger challenge is maintaining accurate, consistent and well-controlled information. Auditors are now able to harness AI to analyse larger volumes of data, making it easier to identify problems and weaknesses more effectively, especially those that may have previously gone unnoticed. For example, a customer may hold different records across their accounting, CRM and payroll systems, or be missing supporting documentation for transactions or adjustments. While auditors have always reviewed data quality, AI has allowed them to analyse much more.
For finance leaders, this means identifying inconsistencies before the audit begins rather than responding to them during fieldwork.
Greater Visibility Means Greater Audit Scrutiny
Greater visibility often means auditors can identify issues more quickly, leading to more questions about the quality and reliability of reported information.
Organisations may also face more questions about how key financial information is generated, reviewed and validated before it reaches management or the board.
Documentation Helps Build Audit Confidence
Further, documentation is critical. Finance teams need to demonstrate how key reporting decisions were made, reviewed and approved. Auditors may also seek additional audit evidence, being the information used to verify financial reporting is complete and accurate. It is important to maintain clear supporting evidence, documented processes and well-defined controls to maintain the quality and reliability of the financial information that will withstand greater scrutiny.
Well-documented processes can reduce audit queries and support a more efficient audit process.
Strong Controls Support Stronger Audit Outcomes
Organisations that maintain strong reconciliations, approval processes and reporting controls are likely to spend less time responding to audit queries and more time focusing on business performance.
This will ensure that all financial reports maintain consistent quality despite the changing technology.
Why Governance and Oversight Matter More Than Ever
Strong governance remains at the core of financial reporting. AI may be used by Auditors to review larger amounts of information, but accountability remains with management, boards and audit committees.
Audit committees should regularly ask management how AI is being used within financial reporting processes, whether controls remain effective and how key decisions can be explained and evidenced.
Boards don’t need to become experts in AI, but they do need to understand and verify how information and decisions are being generated, reviewed and approved. Understanding where and how the technology is being used, who is responsible for oversight and how are they managing risk.
Data governance is also becoming a strategic issue. Inconsistent data, unclear ownership and weak reporting processes can affect everything from decision-making and compliance to audit outcomes. As auditors gain greater visibility into organisational data, weaknesses in governance may become more apparent.
Organisations with clear accountability, robust controls and strong governance frameworks are likely to be better positioned to meet rising audit expectations and provide stakeholders with confidence in the integrity of their reporting.
Five Questions Every Organisation Should Ask Now
Organisations need confidence that their data, processes and governance frameworks can support greater scrutiny. Asking a few practical questions now can help identify potential gaps before the next audit cycle begins.
- How reliable is our financial data?
If information is inconsistent, incomplete or difficult to reconcile, it can create additional audit queries and reduce confidence in reporting. - Are reporting processes documented?
Organisations that rely on informal knowledge or undocumented workarounds may find it more difficult to demonstrate how information is produced and validated. - Do we understand where AI is being used?
Leadership teams should understand where it is being applied and what governance measures are in place. This includes AI used in finance, reporting, forecasting, risk management and operational processes that may influence financial decisions. Oversight starts with visibility. - Can we explain key decisions and outputs?
This is often referred to as explainability. Organisations should be able to explain how outputs were generated, what data was used and who reviewed important decisions. Stakeholders, auditors and regulators increasingly expect organisations to understand the information and processes that support important decisions. - Are governance responsibilities clearly assigned?
Boards, audit committees, executives and operational teams should understand their respective responsibilities for data quality, reporting integrity and risk management.
Preparing for the Next Audit Cycle
As audit expectations continue to evolve, organisations should focus on strengthening the fundamentals that support reliable financial reporting. Before the next audit cycle, consider:
- Reviewing data quality to identify inconsistencies, gaps or duplication across key systems.
- Documenting critical reporting processes so decisions, approvals and controls are clearly evidenced.
- Assessing financial controls to ensure they remain effective, consistently applied and fit for purpose.
- Clarifying governance responsibilities so accountability for data, reporting and risk management is clearly defined.
- Understanding where AI is being used across the organisation and ensuring appropriate oversight is in place.
- Seeking independent assurance where appropriate to review reporting controls, governance arrangements or areas of heightened audit risk.
By addressing these areas proactively, organisations can reduce audit surprises, improve confidence in their reporting and be better prepared for a more data-driven audit environment.
Work with Moore Markhams
At Moore Markhams, we help organisations strengthen reporting processes, governance frameworks and audit readiness. If you would like to review your controls, reporting practices or preparedness for evolving audit expectations, our team can help identify practical opportunities for improvement. Contact your local Moore Markhams advisor to start a conversation.
