Internal audit is advancing beyond manual sampling and retrospective reviews to embrace a strategic approach leveraging artificial intelligence (AI) tools. These technologies employ the use of machine learning algorithms, natural language processing (NLP), robotic process automation (RPA), and advanced data analytics to detect patterns, flag data anomalies, and even provide predictive insights. AI-powered systems can sift through both structured and unstructured data to give you deeper intelligence into financial transactions, operational processes, and regulatory compliance.
But incorporating AI into internal audit practices isn’t simply about speed or automation, it’s about expanding what internal auditors can do and how thoroughly they can do it. By processing more data with more accuracy, and in less time, AI supports decision-making and broader audit coverage without an increase in cost. It enhances the capabilities of internal auditors while keeping their expertise at the core.
Understanding when to apply AI is a strategic decision based on your internal audit goals and desired outcomes. Here’s how this approach leads to a more thorough internal audit and better value for your investment.
Creating a more thorough audit
Due to their speed and efficiency, AI-powered tools enable internal auditors to expand the scope of their audits, assessing more risk factors, resulting in audits that are more thorough, with fewer blind spots and enhanced compliance. No system catches everything, but AI lowers the risk of missing gaps with comprehensive analysis and real-time anomaly detection. AI systems are equipped with advanced algorithms that can continuously monitor data streams. They can detect anomalies and deviations from established norms in real-time, allowing for immediate investigation and resolution.
AI can be used on a range of tasks in your internal audit function.
- Risk assessment and fraud detection: Unlike traditional sampling, AI can analyze entire historical datasets, efficiently identifying unusual transactions or patterns that may indicate fraud or financial misstatements. This broader analysis enhances accuracy and uncovers risks that manual processes might miss.
- Continuous auditing and monitoring: AI allows for real-time auditing, enhancing proactive risk management and enabling the organization to respond more quickly to risks detected. AI enables organizations to continuously track performance metrics, data quality, and anomalies. Additionally, organizations can set up automated alerts to notify relevant stakeholders of any detected issues, enabling swift responses. To leverage AI effectively, organizations need robust data integration platforms that can aggregate data from various sources. These platforms ensure that the AI systems have access to comprehensive and up-to-date information. Additionally, ensuring data security and compliance with regulatory requirements is crucial. Organizations should implement tools that monitor data access, enforce security policies, and ensure compliance with relevant regulations.
- Predictive analytics: Machine learning models can forecast potential risks and help your organization take preventative action before issues escalate. Machine learning algorithms can process vast amounts of data to detect subtle correlations and anomalies that might be missed by manual internal auditing methods. For example, AI can analyze transactional data to predict fraudulent activities before they occur. Once potential risks are identified, organizations can implement preventative measures to address them before they escalate.
- Data extractions and processing: AI can extract data from unstructured sources such as contracts, invoices, and emails, speeding up document review and analysis.
- Streamlined reporting: AI tools can simplify how audit findings are shared by offering recommendations, speeding up report creation, enhancing efficiency, and maintaining reliance on auditors for expert guidance.
- Automation of repetitive tasks: AI-powered tools can automatically extract relevant information from financial statements, contracts, and other documents using optical character recognition (OCR) technology. AI can also automate repetitive transaction testing by sifting through thousands of transactions quickly, identifying anomalies, and reducing the time spent on manual review. By automating these repetitive tasks, AI allows internal auditors to focus on more strategic activities, such as interpreting and confirming AI-generated insights as well as providing advisory services, ultimately enhancing the overall quality and efficiency of the audit process.
AI in action
Using advanced analytics tools, organizations can leverage AI to detect issues with the accuracy of financial reporting through 100% transaction testing instead of relying on sampling. Here’s how AI supports such audits:
- Detect unusual journal entries. AI can support audit planning by surfacing patterns and outliers that inform topic exploration and risk areas. Instead of flagging off-hours entries — which are common in remote environments — AI helps auditors explore new areas, gather supporting data, and cite the sources for documentation. AI scans for anomalies such as round numbers, uncommon account combinations, duplicate entries, or postings on weekends or holidays.
- Identify irregular users. AI can focus on unusual behavior like irregular user activity or atypical account combinations to guide program development.
- Pinpoint variances. AI compares subledger reports against the general ledger to identify discrepancies.
- Develop trend analysis. AI may help track patterns in account balances or transaction volumes, flagging unusual spikes for deeper investigation.
While most AI tools focus on analyzing transactions or spotting anomalies, others are built to support the auditors themselves. One of the most practical, and accessible, examples? AI chat assistants.
Real-time support through your internal data
AI chat assistants act as always-on resources, helping you to quickly assess the information buried in your own systems. Instead of digging through historical reports, audit documents, or regulations, you can ask directions and get fast, context-specific answers. It’s a simple and efficient way to reduce friction.
How to use AI chat assistants
- Policy clarification: Ask about specific internal control procedures and get instant responses sourced from your organization’s policy documents.
- Regulation lookups: Quickly reference relevant standards or past guidance without toggling between systems.
- Knowledge transfer: Use the assistant to explore past audit findings and internal SOPs — especially helpful for onboarding or cross-training teams.
AI chat assistants can help reduce confusion by placing institutional knowledge at your fingertips. When used effectively, all of these capabilities not only improve the precision of financial reporting but also enhance the efficiency of the audit process.
Can an internal audit be fully automated?
Despite AI’s capabilities, internal audit can’t be fully automated. While AI excels at data analysis and automation of repetitive tasks, it lacks the human judgment, professional skepticism, and ethical reasoning that auditors bring to the table. Experienced auditors also bring industry-specific knowledge that AI can’t replicate. Regular reviews by experienced auditors help validate AI’s conclusions, providing a layer of oversight that enhances the reliability and integrity of the audit process. While AI can identify potential issues and flag anomalies, it may also produce false positives that require human intervention to investigate and contextualize them. This collaboration ensures that audit results are both accurate and comprehensive.
In addition to lacking the critical thinking skills of a human, AI has other limitations. Here are some important areas to watch.
- Data quality dependence: AI models rely on clean, accurate data to perform effectively. For example, OCR-scanned PDFs, Word documents, and Excel files can provide structured and usable information if accurately processed. Poor data quality, however, can result in incorrect insights, underscoring the need for high-quality inputs.
- Cybersecurity risks: AI systems aren’t immune to cyberthreats, but firms using approved LLMs can mitigate risks with the right safeguards. Working with a trusted partner to manage cybersecurity helps mitigate this risk.
- Human oversight requirement: AI efficiently flags anomalies but may also generate false positives, making human intervention essential. Auditors investigate flagged items, apply contextual expertise, and assess their significance. This collaboration ensures audit results are accurate, meaningful, and actionable.
- AI hallucination and bias: AI systems can generate information that’s incorrect, presenting it as if it were factual. For example, a language model might fabricate data or create false citations. Additionally, bias in AI refers to the systematic and unfair discrimination that can occur when AI systems produce results that favor certain groups over others. For these reasons, it’s critical that your internal audit team validates and confirms all AI-generated insights for accuracy.
Even with limitations, AI has the power to transform your audit in beneficial ways — but realizing those benefits requires a thoughtful approach for its application in your business.
Applying AI with purpose in internal audits
AI in internal audits isn’t a one-size-fits-all solution — its value depends on how well it aligns with your organization’s specific needs. Simply importing data into an AI platform without a defined strategy can lead to ineffective analysis and overlooked risks. By prioritizing a structured approach, you’ll ensure that AI is used where it adds the most value. Here are four steps to ensure effective implementation of AI in your audit.
- Utilize AI to define audit focus: Leverage AI to analyze large datasets and identify key areas needing attention. AI can detect unusual patterns or anomalies that might otherwise go unnoticed.
- Engage internal stakeholders with AI insights: Use AI-driven analysis to align audit goals, risks, and compliance requirements, ensuring informed agreement on key priorities.
- Customize AI criteria for audit objectives: Define data points, trends, or anomalies for AI to analyze, tailoring the algorithm to meet specific audit needs and objectives.
- Develop an AI-focused audit plan: Determine how AI will complement sampling methods and testing approaches and select advanced tools to support data-driven analysis.
Remember — AI doesn’t drive the audit — human auditors do. Only use AI where it will enhance efficiency, accuracy, or risk detection.
Who you work with matters
AI is on its way to becoming a standard tool in internal audits. Audits leveraging AI uncover hidden risks, enhance compliance, and bring greater precision to every review. But technology alone isn’t enough. Human guidance keeps results aligned with your goals and grounded in strategy. When paired with experienced professionals, AI becomes a tool for smarter audits, not just faster ones. The true advantage comes from investing in a thoughtful approach — and a partner who knows how to make AI work for you.
