How Internal Audit Can Work Smarter, Not Harder with Analytics

In today’s business environment with an unprecedented explosion of data and rapidly emerging applications for Artificial Intelligence (AI), the internal audit function finds itself facing a tremendous opportunity. Traditional, manual audit methodologies, often reliant on limited sampling, are increasingly insufficient to provide the assurance and insights that businesses demand. The power of audit analytics, supercharged by AI, is no longer a futuristic concept; it's today’s reality for internal audit teams striving to work smarter, not harder.

Embracing this transformation isn't as simple one might think. As a recent webinar, "The Future of Audit Analytics – Working Smarter, Not Harder" hosted by the IIA highlighted, navigating the hype and implementing practical solutions requires a nuanced understanding of critcal aspects beyond just technology. Featuring insights from Alban Clot, Founder and co-CEO of Supervizor, the webinar provided an actionable roadmap for leveraging audit analytics effectively. Success hinges on more than just tools – it requires addressing deep-seated challenges in data, skills, and relationships.

The AI Paradox: Why Internal Audit Can't Afford to Be Left Behind

Alban kicked off the webinar by discussing what he calls the "AI Paradox." While AI is being massively adopted across corporations, revolutionizing business operations and even creating new business models, its adoption within internal audit practices is still in its nascent stages. Polling during the webinar underscored this, revealing that more than a third of internal audit attendees are not yet actively using AI in their daily work.

This disparity, Alban warned, comes with significant risks of falling behind:

• Talent Risk: The internal audit profession faces a talent exodus. Many professionals are considering changing jobs, not to leave audit entirely, but to join organizations that have embraced technology and its benefits. Manual, repetitive tasks like recalculating files from disparate systems are tedious and a major driver of dissatisfaction, leading to a "boring" perception of the role.
• Emerging Risks vs. Old Tools: Modern risks, particularly cyber threats, cannot be effectively fought with old-fashioned tools. Relying on traditional sampling methodologies for complex, high-volume data means auditors are likely to miss critical issues. As Alban pointed out, if you sample 20 invoices out of a million, how can you be sure you're not missing something? Furthermore, human bias can creep into sampling criteria, allowing recurring, smaller frauds to slip through the cracks.
• Irrelevancy Risk: Even if auditors manage to find some problems, they might miss even more other critical issues. The Association of Certified Fraud Examiners (ACFE) highlights a concerning statistic: only 16% of fraud is actually detected by internal audit. This low detection rate, coupled with the fact that fraud is typically discovered 12 months after it first occurred, poses a huge problem for internal audit's relevancy and ability to provide timely, impactful insights.

To maintain talent, address emerging risks, and ensure continued relevancy, internal audit simply must embrace audit analytics.

Unpacking the Top Challenges in Adopting Audit Analytics

An additional polling question reinforced Alban's observations about the hurdles internal audit teams face in their analytics journey. The top challenge cited by nearly 50% of attendees was accessing and ensuring the quality of relevant data. This was closely followed by a lack of team skills or training in analytics tools and techniques.

Alban elaborated on these persistent problems:

• Data Fragmentation: Modern global corporations operate across different countries, using multiple ERP systems (like SAP, Oracle, etc.). This fragmentation makes reconciling data across various files and formats incredibly difficult. While capturing the data might be feasible, the true challenge lies in making it usable. Alban shared an anecdote of a German retail company that spent two months just getting their data, only to find they didn't have the tools (like Excel) to even open a file consisting of millions of lines.
• Lack of Tools and Skills: Even with access to data and the ability to open large files (e.g., SQL Server), internal audit teams often lack the necessary skills to script, test, and analyze the data effectively. Building robust scripts that can run properly on massive datasets requires a unique blend of deep business understanding and technical skills.
• False Positives and Demonstrating Value: The final hurdle, even if data is accessed and analyzed, is ensuring the output is valuable. Analytics might generate many false positives, or the insights discovered might already be known to the business, leading to skepticism and making it hard to demonstrate ROI or secure additional budget.

Practical Tips for Building an Effective Audit Analytics Program

To overcome these challenges, Alban shared a handful of tips gleaned from implementing hundreds of audit analytics programs:

Data Quality as the Foundation

This is non-negotiable. As the old adage goes, "garbage in, garbage out." If you feed your analytics program (or even an AI tool) bad data, you'll get garbage insights. For this reason, it is estimated that 80% of an audit team's time is spent on data capturing, cleaning, and preparation—not on actual analysis.

• Connect to Source Data: Always strive to connect directly to the source data. Receiving files via email from multiple intermediaries increases the risk of corruption and compromises data integrity. Direct access ensures you're working with the most reliable information.
• Data Governance and Standards: Establish clear data governance policies and standards. This means speaking the same language as IT and business teams, using consistent terminology, and defining data fields uniformly.

Cultivate Data Literacy

While you don't need to turn every auditor into a data scientist, a certain level of data literacy is crucial. Understand the meaning and context of data sets, even technical terms like "BKPF" in SAP. A basic understanding of data fields (like the difference between internal and external invoice numbers) can make or break the effectiveness of a simple routine check for duplicates.

Share Your Wins: Foster a culture of continuous learning and knowledge sharing. When someone on the team achieves an analytics success, share it widely. This not only educates peers but also builds momentum and enthusiasm for the program.

Cultivate Relationships with Data Owners

Gaining trust and support from data owners and IT teams is paramount. Spend time with them, explain your objectives, and demonstrate how audit analytics can benefit them directly. Alban shared an example of an automotive company where demonstrating how analytics could help IT teams gain insights from audit routines unlocked their support. Talk to everyone involved—from management to developers—to understand the business process and gain holistic support.

Start Small and Standard, Then Scale

Don't try to do everything at once. Select a supportive geography, a small ERP system, or a well-understood process where you can achieve quick wins and demonstrate value. This early success builds confidence and secures the buy-in needed to scale the program globally. An engineering firm started their program in France, achieved success in three months, and then recently expanded globally to 25 countries. Focus on building standard checks first, as these are easier to deploy and demonstrate immediate value.

Beyond Traditional Sampling: The Power of Technology

The webinar emphasized that while human elements are crucial, purpose-built technology plays a vital role in overcoming the inherent challenges of data quality and traditional sampling. Solutions that can automatically standardize and normalize data from disparate systems are game-changers.

This kind of technology, like that from Supervizor, connects directly to transactional systems (like SAP, Oracle, and other applications) and retrieves data from various modules (e.g., A/P, A/R, G/L). The platform then standardizes this fragmented data into a unified data lake. This automated process handles the complex task of reconciling data from different models and formats, turning unusable datasets into a single, cohesive source for analysis. For example, whether an invoice is entered in SAP in the US or in Germany, such a platform ensures it's stored identically, allowing for consistent routine checks across the globe. This significantly reduces the 80% of time typically spent on data capturing, cleaning, and preparation, allowing auditors to focus on analysis and insights.

Such solutions also provide a library of pre-set, ready-to-use controls and routine checks (e.g., Supervizor currently offers 350+ out-of-the-box checks categorized by process like P2P, R2R, R2C). This empowers audit teams to move beyond traditional sampling to full-population testing, continuously monitoring data and identifying risks and anomalies much more rapidly.

The Payoff: Protecting Your Brand, Building Trust

An effective audit analytics program delivers tangible benefits for the entire organization:

• Switch from Sampling to Full Population Testing: This fundamentally reduces the risk of missing critical issues and provides higher assurance.
• Rapid Fraud and Pattern Detection: Continuous control over all transactional data enables much faster detection of fraud and other anomalies.
• Enhanced Brand and Trust: Protecting the organization's brand, building investor trust, and instilling confidence in financial numbers are ultimate outcomes of a robust, data-driven audit function.

Establishing a successful audit analytics program is a journey that requires a blend of strategic vision, investment in human capital, and the right technological enablers. It's about transforming internal audit from a reactive, sample-based function to a proactive, data-driven strategic partner. By prioritizing data quality, fostering data literacy, building strong relationships, starting small, and leveraging purpose-built technology, internal audit teams can navigate the complexities of the modern data landscape and work smarter, not harder.

For a deeper dive into these critical insights and to see practical examples of how internal audit teams are leveraging analytics today, we encourage you to watch "The Future of Audit Analytics – Working Smarter, Not Harder."