Audit Intelligence: How Can Auditors Use AI in Internal Audit?

Studies* show that audits relying solely on manual review often fall short when dealing with massive data volumes. Because of this, errors and inefficiencies naturally follow. This is why many organizations are turning to artificial intelligence (AI) in performing internal audits. From faster data analysis to better risk identification, AI offers a reliable way to improve audit quality and efficiency. 

Here’s how to make it work in practice. 

Practical applications of AI in internal audit

When people hear about AI technologies, they often think about robots or machines replacing people. But the role of AI in internal audit isn’t about taking jobs away, it’s about supporting them. Through this technology, auditors can make the most of their time focusing on more value-adding responsibilities. 

To give you a clearer idea, here are detailed examples of how you can use AI in your audit work. 

1. Automated data analysis for stronger risk checks

Analyzing data from different sources has always been one of the toughest parts of auditing. Traditional approaches mean reviewing samples of data because looking at everything manually just isn’t possible. But this has a drawback. When you only test a fraction of data, you risk missing hidden issues in the rest of it. 

AI changes that.  

With machine learning and advanced data analysis, you can scan 100% of your data population. Instead of pulling random samples, you get to review the entire set of transactions, internal controls, or financial records. 

But will the results be accurate enough? Research says so. A 2025 study shows that AI models can achieve a 95.7% accuracy rate in identifying low-risk filings. The same study also revealed unique behavioral patterns among reporting entities. This shows how much stronger risk assessments can be when you let AI do the heavy lifting. 

To see how it applies in practice, let’s say there are 10 million financial transactions. With traditional audits, you’d pick maybe 1,000 to test. AI can remove such limitations. It can go through all 10 million and highlight the ones that look unusual. That way, you’re not missing red flags hidden outside the sample. 

2. Real-time monitoring instead of waiting for reviews

Another limitation of traditional audits is timing. Most audit work is done periodically. You review processes quarterly, semi-annually, or annually. By the time you find a problem, it’s already months old and may have already caused damage. 

AI flips this around. Its capability to do continuous monitoring allows your internal audit team to keep an eye on processes, transactions, and controls in real-time. In return, they no longer need to wait until scheduled reviews to catch potential issues. 

Real-time monitoring lets you flag: 

• A transaction that’s unusually large compared to past trends. 

• An employee suddenly bypassing a control. 

• A purchase order that doesn’t match approval policies. 

The earliest you can catch these issues, the faster you can step in before the issue grows into a major risk. That’s a huge shift in how audits can protect organizations. 

3. Smarter and faster document reviews

If you’ve ever been buried in contracts, invoices, and policies, you know how draining audit reviews can be. Going word by word through each document eats up days, and when you’re tired, it’s far too easy to miss something important. 

AI-powered document processing makes this much easier. These tools use natural language processing (NLP) to actually understand the context of words. Unlike basic keyword searches, NLP can interpret meaning. 

For example, AI can: 

• Find specific clauses in contracts, like termination terms. 

• Compare policies to identify inconsistencies. 

• Pull financial figures from unstructured documents, such as scanned PDFs. 

AI cuts hours off the review process. You don’t have to read every single page anymore. AI highlights the sections you need, so your energy goes into understanding and deciding what to do next. 

4. Predictive analytics for fraud detection

Any auditor who has reviewed records knows how tough it is to spot fraud. The clues are rarely obvious and are usually hidden across different data points.  

AI makes the job easier. It looks at all the data together, finds patterns, and highlights what seems suspicious. Its predictive analytics feature also gives it the ability to learn from past transactions, thereby warning you about potential risks. 

5. Automated compliance testing

Compliance checks are often repetitive and time-consuming. Auditors have to verify if transactions and activities follow regulations and internal policies. Doing this manually can eat up a huge chunk of audit time. 

AI can take over many of these routine compliance tests. It can automatically compare transactions against rules, flag policy violations, and create exception reports. 

The benefit here isn’t just time savings. It also means compliance checks are more consistent. Instead of relying on individual interpretation, AI applies the same logic to every transaction. Auditors can then focus on understanding and investigating the exceptions. 

The benefits of using AI in internal audit

By now, you’re probably seeing why so many organizations are investing in AI. But let’s look at the benefits of AI in audit in more detail. 

1. Efficiency gains

One of the first things you’ll notice when you use AI to automate routine tasks is how much faster tasks get done. Work that used to take days or weeks can now be done in hours.  

With an increased use of AI, noticeable changes occur over time. Research shows: 

• The chance of having to redo or restate an audit goes down by 5%. This means fewer mistakes are found later.
• Audit fees decrease by about 0.9% on average because AI helps streamline the work.

With AI handling the repetitive tasks, auditors can expand their audit coverage, run checks more often, and put their attention where it matters most. In return, their focus shifts to understanding the results and guiding management with clear insights. 

2. Higher accuracy

Manual work comes with human error. People get tired, distracted, or miss details. AI doesn’t.  

Professionals in auditing report that AI improves both speed and accuracy in the audit process.  

“We find the greatest improvements in efficiency, accuracy, and insights when applications connect seamlessly across your entire firm,” shares Cathy Rowe, Senior Vice President and Segment Leader, U.S. Professional Market, Wolters Kluwer Tax & Accounting North America, in this article. 

This means audits are becoming more efficient and more effective at the same time. 

3. Scalable operations

Growing organizations face higher audit demands, but adding staff of the same proportion is rarely practical. AI handles larger workloads without requiring the same increase in manpower.  

For example, KPMG Clara, an intelligent audit platform, uses AI and analytics to enable KPMG’s auditors to automate repetitive parts of their work. Using this tool, this Big 4 firm extends their audit solutions across bigger operations while maintaining consistency. 

The challenges of using AI in internal audit

Of course, the use of AI in internal audit is not the perfect solution. Like other technologies, it also has its downsides. 

1. Bias in AI algorithms

AI learns from past data. If that data has bias in it, the AI will likely pick it up and repeat it. If your historical data contains biases, AI might unfairly flag or ignore certain risks. Such biases can lead to unfair targeting of specific departments or employees. In some cases, it might also miss genuine risks in areas that historically appeared low risk.  

To avoid this, you need to conduct periodic reviews and tests of your AI tools to check for algorithmic biases. Auditor expertise must also be present throughout. Both of these are critical moves to ensure regular testing and adjustments are necessary to keep the outputs balanced and accurate. 

2. Data quality issues

AI is powerful, but it cannot rise above the quality of the data it reviews. Poor data means poor outcomes. If you start with incomplete or inconsistent records, the tool may give you results that are confusing or wrong. You could end up chasing risks that don’t exist or missing the ones that do.  

To make sure AI gives you useful results, it’s essential to have strong controls around your data. When the information is clean and dependable, the AI can do its job properly. 

3. Technical complexity

AI tools are not always simple to use, especially if your audit team does not have technical expertise in using this technology. Afterall, most auditors have limited background in data science or coding.    

This skill gap can create dependency on external vendors. At the same time, it also pushes you to make significant investments in training or bringing in outside specialists so your auditors can get the most value from AI without confusion or errors. 

4. Overreliance on AI

When using AI, there’s a risk that you may become overly dependent on it. This can potentially reduce your professional skepticism and critical thinking skills. Of course, you don’t want to lose the actual skills that make you valuable in the first place.   

Relying fully on AI without a second thought can be risky. Its results are useful, but they should always be reviewed with professional skepticism.  

Your expertise is what makes the final audit judgment reliable. 

5. High implementation costs

AI can offer long-term cost savings, but its initial implementation costs can be substantial.  

At first, you might find it challenging to justify these upfront investments. Benefits may take time to materialize, so it can also be difficult to get the management’s approval of this additional investment. 

Best practices for using AI in internal audit

If you want to get the most out of AI, you need a solid plan. Here are best practices that can help: 

1. Start with a roadmap

Before you bring AI into your audit work, it’s important to have a clear plan. The strategy should connect with both your audit team’s goals and your company’s bigger tech direction.

A good starting point is to look at the challenges in your current process, like reviewing huge volumes of transactions or spotting unusual activity. Instead of rolling out AI everywhere at once, try it first on smaller projects. This gives you time to test, adjust, and show results before expanding. 

2. Prioritize data quality

AI can only deliver good results if the data it works with is accurate.  

When the information is messy, outdated, or inconsistent, the results cannot be trusted. This is why strong data governance matters. With clear rules for collecting, storing, and maintaining data, you give AI a solid base to work from and make sure audit findings are reliable. 

3. Invest in staff training and development programs

For AI to really help, your audit team has to know how it works, how to read its results, and when to step in with professional judgment.   

This means setting up strong training programs that cover both the technical side of AI and how to use it in actual audits. With the right knowledge, you can use AI with confidence instead of second-guessing it. 

4. Validate results

AI tools should not run unchecked. Regular comparisons between AI findings and manual audit work help confirm that the results are accurate. You must also conduct regular independent reviews of AI-generated findings to ensure you’re getting accurate results.  

This process builds confidence in the system and ensures that AI does not create blind spots. Validation also helps catch errors early before they lead to poor decisions. 

5. Maintain professional skepticism and human oversight

Even with AI, auditors still need to apply judgment and skepticism. That means you cannot just rely on AI results without review.  

Clear protocols should explain when auditors must step in to analyze further. Teams should also be aware that AI has limitations and cannot capture every nuance. With a structured decision-making framework, you can combine the strengths of AI and human expertise to maintain audit quality. 

6. Stay compliant

AI can improve audits, but only if it’s used within the boundaries of regulations and privacy rules.

Overlooking these requirements could cost your organization money and credibility. That’s why auditors should collaborate with legal, compliance, and risk functions. Doing so ensures AI contributes to stronger audits without adding extra risks. 

7. Document everything

To use AI responsibly, you need to be transparent, which means recording how the system operates, what inputs it uses, and how it arrives at its conclusions.  

These records help with reporting, compliance, and stakeholder trust. AI is most valuable when it’s combined with good data quality and professional judgment, making audits more focused and effective. 

AI works best when paired with clean data, strong governance, and human judgment. When used properly, it helps auditors focus on providing insights, protecting organizations, and ensuring compliance. 

Transform your audit practice with expert F&A support

If you’re ready to see how AI in internal audit can transform your processes, get in touch with our team to learn more about our audit support solutions.  

Our audit professionals at D&V Philippines specializes in assisting firms adopt AI into their audit processes.  

You can also download a free copy of our Seasonal Audit Support whitepaper to explore the solutions we have in store for you. 

*Source: Sheffield, Marlowe & Stoddard, Caelan & Chitale, Raghunandan & Nakato, Grace. (2019). HOW BIG DATA ANALYTICS IS RESHAPING AUDIT 4.0 BY ENHANCING RELIABILITY AND STREAMLINING AUDIT OPERATIONS. 1. 68-77. Accessed at https://www.researchgate.net/publication/386028688_HOW_BIG_DATA_ANALYTICS_IS_RESHAPING_AUDIT_40_BY_ENHANCING_RELIABILITY_AND_STREAMLINING_AUDIT_OPERATIONS_1  

This article was first published on 21 December 2018 and has been updated since then for relevancy and comprehensiveness. 

Last update: 27 August 2025 

Updated and edited by: Mary Milorrie Campos