Audit Analytics Expert Shares His Vision for Internal & External Audit

Jamie Crossman-Smith brings 20 years of experience in audit and risk management, having worked with top-tier firms such as Deloitte, Barclays, Morgan Stanley, and Citigroup. He currently leads Bloch.ai, a KNIME partner focused on AI-enabled innovation.

Learn why he thinks audit teams need to gain advanced analytics skills, why successful audit analytics requires more than technical skills, and how his most recent book, “KNIME for Auditors” helps.

The stakes are high. Audit teams are increasingly recognised as strategic partners in organizational oversight, risk management, and decision-making. The IIA’s 2024 Global Internal Audit Standards reflect this evolution, emphasizing the ethical, data-driven insights that align with strategic goals, which auditors are now expected to deliver. 

However, both internal and external audit teams face shared challenges — limited resources, reliance on legacy tools, and difficulty adapting to the pace of technological change. These pressures threaten their ability to adapt to this new era and could erode the trust and relevance of audit teams in the eyes of stakeholders.

Hear Jamie Crossman-Smith discussing real-world applications of GenAI with peers from Finance, Auditing, and AI on: Successes, Challenges, and Limitations.

For internal auditors, the need to move beyond compliance checks to deliver forward-looking insights and strategic value is clear. Teams are often held back by limited resources, reliance on outdated tools like Excel and legacy systems, while many Chief Audit Officers remain unaware of what modern analytics can deliver.

For external auditors, concerns persist around widespread fatigue with the dominance of the Big Four accounting firms, audit quality, and an increasing number of financial restatements due to errors in audited accounts.

At the core of both internal and external audit is data.

The sheer volume, variety, and complexity of today’s data requires sophisticated tools to analyse, interpret, and report effectively.

While large organizations are forging ahead, leveraging substantial budgets to develop bespoke applications and explore advanced capabilities like machine learning and data science, for most internal audit functions, the reality is far less cutting-edge.

Many teams still rely on tools like Excel or legacy software such as ACL and IDEA, and even these are often used sporadically. It’s common to find  a sole “analytics champion” managing tools likeSQL and  Power BI, but without broader organisational support. This leaves teams unable to fully leverage modern data science or AI in their work.

Traditionally, both internal and external audit teams provide assurance by evaluating whether controls are in place and functioning effectively.

This core responsibility will remain essential, however, with advanced data analytics and machine learning techniques in their skillset, auditors can not only analyze historical trends, but identify emerging risks, and develop predictive models to provide forward-looking insight.

Machine learning techniques enable auditors to analyze much larger volumes of data than before, and uncover new risks and trends that might otherwise go unnoticed. Auditors can learn to build, train, and deploy models that learn from data, adapt to new information, and continuously improve.

With these techniques, auditors can truly take on a more strategic role by providing the forward-looking insights and actively shape business strategy.

Let’s look at a couple of examples.

• Fraud detection: Consider the impact of machine learning on fraud detection. By training models to identify unusual patterns and behaviours, auditors can flag transactions with a high likelihood of fraud in real time.
• Risk assessment: Similarly, predictive models can highlight operational or financial risks before they escalate, allowing auditors to alert management or external stakeholders to potential issues well in advance.
• Environmental, social, and governance (ESG) audit: With the integration of machine learning models into audit processes, auditors can assess sustainability risks or social impact by analyzing complex datasets that capture factors such as carbon footprint, community engagement, and supply chain integrity. These techniques enable auditors to provide dynamic, data-driven insights that evolve with organizational priorities and risk landscapes.

These capabilities shift the role of auditors from verifying past performance to proactively guiding organisations toward a more secure future.

Organizations need to enable entire teams to become “analytics champions.” This requires a tool that is user-friendly and intuitive and provides the functionality teams need to tackle everything from compliance, to risk assessment, to fraud detection, to ESG reporting, and more.

My work at Bloch.ai  helps organizations streamline their operations, gain

deeper insights, and maintain robust governance practices. This is underpinned by KNIME’s accessible analytics platform. We specialize in using KNIME for internal audits, SOX compliance, Continuous Monitoring, and AI-driven methods that make complex monitoring and controls more efficient.

As an open-source data analytics tool, KNIME ticks all the boxes. It’s based on an intuitive visual programming environment that enables the most sophisticated data science work, while removing unnecessary technical complexity.

Get new users up to speed quickly: Workflows are created in a no-code fashion through an easy and intuitive drag-and-drop interface, allowing sophisticated data work without requiring advanced coding skills. This reduces adoption hurdles, allowing new users to get up to speed quickly and helping organizations see results faster.

Gain visibility and control: Data transformations are never hidden behind a black box, giving audit teams full visibility and control over their workflows.
Benefit from cost effectiveness: Thanks to this open architecture, you benefit from a much lower total cost of ownership compared to many legacy solutions. Even with commercial offerings like those from Bloch.ai, where additional support, training, and specialized features are available, the overall costs remain considerably lower than many proprietary platforms.

This is all about fostering a culture of innovation across both internal and external audit. 

Successful audit analytics requires more than technical skills; it demands curiosity, creativity, and a willingness to embrace new ideas. KNIME provides the tools, but it is your innovative approach that will deliver the most value to your organisation. 

My book, KNIME for Audit, is aimed to equip both internal and external auditors with the tools and insights needed to navigate this transformation. 

This book provides pre-built workflows and examples for a variety of audit tasks. Auditors can learn how to apply machine learning to a range of audit scenarios from compliance and risk assessment to fraud detection and ESG reporting. We chose these examples as they particularly demonstrate the potential for auditors to become drivers of business intelligence, helping leaders make informed, proactive decisions. 

Rather than viewing the workflows in the book as fixed templates, consider them as starting points. KNIME’s visual environment enables anyone to build and modify the workflows without the need for any advanced programming skills. That makes it ideal for experimenting with the examples. 

As teams progress through this book, they’re encouraged to adapt the workflows, explore different methods, and refine your approach to suit the unique needs of each audit. Tailor them to your specific data and objectives, and don’t hesitate to try new techniques. Every audit is different, and some of the most valuable insights come from taking a creative approach to data and analysis.

Redefine the roles of internal and external audit

Use this book as your guide but feel empowered to go beyond it and redefine the roles of internal and external audit in today’s data-driven world. Welcome to the future of auditing — a future where data-driven insights, machine learning, and a culture of experimentation set new standards for risk management, oversight, and strategic support.