Internal audit teams today face the dual challenge of doing more with less while maintaining high assurance. Automation and AI are emerging as game-changers, boosting both efficiency and audit coverage. In fact, regulators have noted that AI-driven techniques – such as enhanced risk assessment, full-population testing, and other automated procedures – “can significantly reduce the risk of missing irregularities or unusual patterns”¹.

This article explores five key internal audit processes that are ideal for automation – and how artificial intelligence (AI) makes each process more efficient and effective.

1. Transaction Testing

Transaction testing (examining samples of financial transactions for accuracy or compliance) is a core audit activity – and one that’s perfectly suited for automation. Traditionally, auditors could only test a limited sample of transactions manually. AI changes this by enabling 100% transaction testing instead of sampling². By using machine learning and robotic process automation (RPA), internal audit can test entire populations of transactions quickly and consistently, providing far greater assurance than manual sampling ever could³.

An AI tool can sift through thousands of entries in seconds, flagging any anomalies or policy violations for further review⁴. This means routine transaction validation work – such as checking invoices against purchase orders or verifying expense reports – can be performed much faster and with fewer errors.

AI-driven transaction testing doesn’t just increase volume; it improves quality. Advanced analytics can uncover patterns and outliers that a human might miss. For example, AI algorithms can scan all journal entries and highlight unusual ones (e.g. round-dollar entries, postings on weekends, or uncommon account combinations) that might indicate errors or fraud⁵. Some practical anomalies AI can detect include:

Unusual journal entries: Entries made at odd hours or with abnormal values⁶.
Irregular user activity: Transactions initiated by users who don’t typically perform such actions⁷.
Reconciliation variances: Discrepancies when comparing subledger records to the general ledger⁸.
Trend anomalies: Spikes or dips in transaction volumes that deviate from historical patterns⁹.

By automating transaction testing, internal auditors can examine full data sets rather than small samples, which greatly increases the likelihood of catching errors or red flags. As the Acting PCAOB Chair George Botic observed, more efficient population testing and other AI-driven procedures “significantly reduce the risk of missing irregularities” in the audit¹⁰.

2. Risk Assessment

Risk assessment is another audit process ripe for automation. Before audits begin, internal auditors must assess which areas carry the highest risk so they can prioritize their work. This often involves poring over business data, questionnaires, and prior audit findings – a labor-intensive task that AI can streamline. AI-based predictive analytics and process mining tools can analyze vast quantities of operational and financial data to identify latent risks and trends that humans might overlook¹¹.

Unlike traditional risk assessments that rely on limited data and manual judgment, AI can efficiently evaluate entire datasets (e.g. all transactions over several years) to spot outliers or correlations indicative of risk¹³. For example, AI might flag an unusual pattern of payments just below approval thresholds across departments, suggesting a potential control workaround or fraud scheme. All this adds up to a more informed risk assessment. Auditors can use these AI insights in planning, focusing their audit scope on high-risk areas that truly warrant attention¹⁴.

By automating parts of the risk assessment, internal audit can continuously update its risk view. AI agents can scan news, regulatory updates, and internal KPIs to alert auditors of new risks in real time¹⁶ ¹⁷. The result is a dynamic, data-driven risk assessment process that allocates audit resources more effectively.

3. Continuous Control Monitoring

Continuous control monitoring (or continuous auditing) refers to using technology to audit in near real time, rather than through periodic spot-checks. This has long been a goal for internal audit, and AI is finally making it feasible¹⁸. In a manual world, auditors might test a control (say, user access rights or transaction approvals) once or twice a year. With AI-driven automation, those controls can be monitored 24/7, with exceptions flagged immediately.

AI systems with advanced algorithms can continuously monitor data streams, detecting any anomalies or deviations from established norms in real time and triggering alerts for investigation¹⁹. This means potential issues – like a purchase transaction exceeding an approval limit or an unauthorized system configuration change – are caught and addressed faster, reducing risk exposure. Essentially, automated monitoring serves as an ever-vigilant third eye. Internal auditors can then focus on investigating the flagged exceptions and improving processes, rather than spending time manually sampling transactions.

4. Evidence Collection and Documentation

Gathering evidence is a fundamental yet time-consuming part of every audit. Internal auditors must collect and review documents – policies, invoices, contracts, logs, emails, you name it – to verify that processes and controls are operating correctly. Here, AI can act as a tireless research assistant, dramatically speeding up audit evidence collection and documentation. AI tools (especially those using natural language processing and intelligent search) can automatically extract relevant data and documents from multiple sources – from ERP system logs to email archives – and organize that information for auditors²¹.

By automating evidence collection, AI helps ensure completeness and accuracy of audit documentation. Auditors receive the information they need faster and in a more digestible format, allowing them to spend their time analyzing the evidence rather than gathering it. Additionally, AI can aid in documentation drafting – for example, transcribing meeting notes or walkthrough interviews and then summarizing them²³. In short, AI reduces the grunt work of compiling audit evidence and lets auditors concentrate on interpreting the evidence and exercising their expertise.

5. Reporting and Follow-up

Yes – AI can play a helpful role in audit reporting, issue tracking, and even stakeholder communications. Drafting audit reports or summarizing findings is a task that AI text-generation tools can help expedite. For example, AI-driven reporting tools can analyze audit results and offer initial recommendations or summaries, which auditors can then refine. This speeds up report creation and ensures key insights are highlighted, all while keeping the auditor in control of the final narrative²⁴.

Beyond the report document itself, automation can help with the follow-up process after an audit. Tracking management’s remediation of audit findings often involves lots of emails and status updates. Here, AI-powered workflows or even chatbots can lighten the load²⁵. In one innovative example, an audit function even experimented with a virtual AI avatar to conduct the opening meeting of an audit, which saved the auditors’ time for more value-added discussions later²⁶.

Conclusion: Smarter Audits with a Human Touch

Internal audit is evolving, and automation is at the forefront of that change. By automating processes like transaction testing, risk assessment, continuous control monitoring, evidence collection, and reporting, audit teams can achieve broader coverage and higher efficiency than ever before.

Yet, it’s clear that AI doesn’t replace the need for human auditors – it enhances it. Automation provides the data crunching and speed, but internal auditors provide the skepticism, context, and ethical considerations that machines lack²⁸. The most successful internal audit functions will be those that strike the right balance: leveraging AI for what it does best and relying on experienced auditors to interpret results and make sound decisions.