Over the past few years, artificial intelligence (AI) has evolved far beyond being a supporting technology for improving efficiency or accelerating business processes. It has become an integral part of organizational decision-making. From preparing financial reports and analyzing data to automating processes and forecasting future trends, AI applications are expanding at an unprecedented pace.
As this adoption continues to grow, a new question has emerged at the forefront of discussions among boards of directors, audit committees, risk management teams, and external audit firms:
Does the increasing reliance on AI elevate the risk of material misstatement, or does it present an opportunity to enhance financial reporting quality and improve audit effectiveness?
There is no universal answer. The risks do not arise from artificial intelligence itself, but rather from how it is designed, implemented, and used, the quality of the data that powers it, and the governance and oversight surrounding its operation.
For this reason, the discussion is no longer centered on whether organizations should adopt AI. Instead, the focus has shifted to how AI can be implemented in a way that reduces risk rather than introducing new layers of complexity and uncertainty.
What Is a Material Misstatement?
Before examining AI’s impact, it is important to understand the concept of material misstatement, one of the most fundamental principles in the International Standards on Auditing (ISAs).
A material misstatement refers to any error, omission, or inaccurate presentation of financial information that could reasonably influence the economic decisions made by users of the financial statements. Such misstatements may result from unintentional errors—such as processing or estimation mistakes—or from deliberate fraudulent activities intended to mislead users of financial information.
The term material does not simply refer to the size of an error. Rather, it reflects the extent to which that error could influence the decisions of investors, lenders, regulators, and other stakeholders.
Accordingly, the International Standards on Auditing, particularly ISA 315 (Identifying and Assessing the Risks of Material Misstatement), require auditors to obtain a thorough understanding of the entity and its environment, identify and assess the risks of material misstatement, and design appropriate audit procedures in response to those risks.
As AI becomes embedded in a growing number of financial and operational processes, assessing these risks has become significantly more complex than ever before.
How Has Artificial Intelligence Changed the Risk Landscape?
Traditionally, most financial reporting risks stemmed from human error, weaknesses in internal controls, or intentional manipulation of financial information. Today, however, organizations face an entirely new layer of risk one that originates from intelligent systems themselves.
Many organizations now rely on AI models to classify data, analyze transactions, detect anomalies, generate financial forecasts, and even assist in preparing certain financial reports.
As a result, any weakness in the AI model, the data used to train or operate it, or the interpretation of its outputs can directly affect the quality and reliability of financial information.
In other words, the key question is no longer limited to: “Are the data accurate?”
It has expanded to include another equally important question: “Is the system producing those data operating correctly?”
This shift presents a new challenge for both auditors and risk management professionals. Their responsibility is no longer confined to evaluating financial outputs alone; they must also assess the reliability, integrity, and trustworthiness of the AI models that generate those outputs.
How Can Artificial Intelligence Increase the Risk of Material Misstatement?
Despite the remarkable capabilities of AI, deploying it without robust governance and oversight can introduce entirely new forms of material misstatement risk that were largely absent in traditional financial reporting environments.
1. Reliance on Poor-Quality Data
Artificial intelligence does not generate knowledge independently. Its performance depends entirely on the quality of the data used to train or feed the model. If that data is incomplete, inaccurate, outdated, or inherently biased, the resulting outputs are likely to be equally unreliable.
This reflects the well-known principle in data science: Garbage In, Garbage Out (GIGO); poor-quality data inevitably leads to poor-quality outcomes.
Within a financial reporting environment, this may result in inaccurate accounting classifications, improper asset valuations, incorrect provisions, or unreliable financial forecasts, all of which can ultimately lead to material misstatements in the financial statements
2. AI Hallucinations
One of the most significant concerns raised by professional bodies in recent years is the phenomenon known as AI hallucinations.
AI hallucinations occur when an AI model generates information that appears logical, coherent, and convincing but is, in reality, inaccurate, fabricated, or unsupported by reliable evidence.
The risk becomes particularly significant when employees, accountants, or financial professionals rely on AI-generated outputs without independently verifying their accuracy, especially when preparing reports, summarizing financial information, or interpreting complex accounting standards.
In such situations, misleading AI-generated content may inadvertently find its way into financial analyses or decision-making processes, increasing the likelihood of material misstatements.
3. Bias Within AI Models
AI models are not inherently objective.
They learn from historical data, and if that data contains embedded biases, flawed assumptions, or unbalanced representations, the model is likely to reproduce and potentially amplify those same biases in future decisions.
In financial environments, biased AI models may produce unfair valuations, inaccurate risk classifications, or distorted forecasts that ultimately affect the quality of financial information available to management and other stakeholders.
Consequently, organizations must recognize that algorithmic outputs are only as reliable as the data and assumptions upon which they are built.
4. Lack of Transparency and Explainability
Many modern AI applications rely on highly sophisticated machine learning models whose decision-making processes are often difficult to interpret.
This creates a significant challenge for auditors.
Professional auditing standards require more than simply accepting a result—they require auditors to understand the evidence supporting that result.
If an AI system cannot adequately explain how it reached a particular conclusion, auditors may find it difficult to rely on that output as sufficient and appropriate audit evidence.
For this reason, AI explainability has become one of the most important considerations when evaluating the suitability of AI tools in financial reporting and auditing.
5. Overreliance on Artificial Intelligence
Perhaps the most critical risk associated with AI adoption is excessive reliance on automated systems.
As users become increasingly confident in AI-generated recommendations, they may gradually reduce their own critical thinking and begin accepting AI outputs without applying professional judgment or assessing whether the results are reasonable.
Within the auditing profession, this could allow material misstatements to go undetected—not simply because the AI system made an error, but because the human reviewer failed to challenge its conclusions.
This is why professional organizations consistently emphasize that AI should support professional judgment, not replace it.
Can Artificial Intelligence Reduce the Risk of Material Misstatement?
While artificial intelligence has introduced new categories of risk that organizations must manage carefully, it has simultaneously transformed the ability of auditors and risk management professionals to identify potential misstatements before they develop into significant issues.
As a result, leading organizations are no longer asking: “Should we use AI?”
Instead, they are asking: “How can we leverage AI to improve audit quality while maintaining effective control over risk?”
AI is not intended to replace auditors or risk professionals. Rather, it equips them with analytical capabilities that were simply unattainable using traditional audit methodologies.
Analyzing Massive Volumes of Data in Record Time
Traditional audits have long relied on sampling, whereby auditors examine a selected portion of transactions and draw conclusions based on those samples.
Today, AI enables organizations to analyze millions of financial transactions within a remarkably short period—reviewing datasets that would otherwise require weeks or even months of manual analysis.
This does not guarantee that every error will automatically be detected.
However, it significantly increases the likelihood of identifying unusual patterns, anomalies, and exceptions that may indicate material misstatements or weaknesses in internal controls.
Consequently, auditors can obtain a broader understanding of organizational risk than was previously possible.
Detecting Unusual Patterns Before They Escalate
The value of AI extends beyond rapid data processing.
Its true strength lies in identifying hidden relationships and behavioral patterns that might easily escape human observation.
For example, AI systems may detect:
- Repeated transactions with nearly identical values.
- Unexpected changes in account activity.
- Transactions processed outside normal business hours.
- Unexplained fluctuations in expense ratios compared with previous reporting periods.
Although these indicators do not necessarily prove the existence of fraud or material misstatement, they enable auditors to focus their attention on higher-risk areas rather than distributing audit effort evenly across all transactions.
This reflects the modern risk-based auditing approach promoted by contemporary auditing standards
From Periodic Auditing to Continuous Auditing
One of the most significant transformations accelerated by artificial intelligence is the shift from periodic auditing to continuous auditing.
Rather than waiting until the end of a reporting period to identify errors or irregularities, organizations can now monitor transactions and business activities in real time. AI-powered systems continuously analyze operational and financial data, generating early alerts whenever unusual indicators or suspicious patterns emerge.
This proactive approach enables management to address issues at an early stage, reducing the likelihood that isolated errors will accumulate and evolve into material misstatements affecting the financial statements.
Ultimately, continuous auditing strengthens both the efficiency of the audit process and the effectiveness of an organization’s internal control environment.
Predicting Risks Before They Occur
Artificial intelligence is no longer limited to analyzing historical information.
Through predictive analytics, AI can identify emerging trends, anticipate potential risks, and highlight areas that are more likely to experience control failures or financial irregularities.
These predictive capabilities allow management to implement preventive measures before problems materialize, representing a significant evolution in the philosophy of risk management.
Instead of reacting to events after they occur, organizations are increasingly adopting a proactive, forward-looking approach focused on anticipating and mitigating risks before they have a meaningful impact.
What Do the International Auditing Standards Say?
As AI technologies continue to evolve, professional standard setters have made it clear that technology does not alter the fundamental principles of auditing; it merely changes the tools used to perform audit procedures.
The International Auditing and Assurance Standards Board (IAASB) reaffirmed through ISA 315 (Revised 2019) that auditors remain responsible for identifying and assessing the risks of material misstatement, obtaining an understanding of the entity and its internal control system, and designing appropriate audit responses, regardless of whether advanced analytical tools or AI-based technologies are used.
The IAASB has also published guidance on the use of Automated Tools and Techniques (ATTs), emphasizing that while technology can significantly enhance the effectiveness and efficiency of audit procedures, it does not replace the auditor’s responsibility to exercise professional judgment and professional skepticism when evaluating audit evidence.
In other words, the International Standards on Auditing do not authorize AI to make audit decisions independently. Instead, they recognize AI as a valuable tool that supports auditors in reaching better-informed conclusions when used within an effective framework of governance, internal controls, and oversight.
Why Will Professional Judgment Always Remain Essential?
Despite the rapid advancement of artificial intelligence, one element of auditing cannot be replicated by algorithms: professional judgment.
AI systems can process enormous volumes of data, identify patterns, and generate analytical insights at extraordinary speed. However, they cannot fully understand business context, interpret complex economic circumstances, evaluate management’s intentions, or weigh conflicting pieces of audit evidence in the same way an experienced auditor can.
For this reason, the final responsibility for audit conclusions will always rest with the auditor, not with the technology.
In fact, as organizations become increasingly dependent on AI, professional expertise becomes even more valuable. Evaluating AI-generated outputs requires a deep understanding of technology, accounting, auditing, internal controls, and enterprise risk management.
The more sophisticated AI becomes, the greater the need for skilled professionals capable of questioning its outputs and validating its conclusions.
The Future of Auditing: A Partnership Between Humans and Artificial Intelligence
Some view artificial intelligence as a potential replacement for auditors or even a threat to the profession. However, the future points toward a very different reality.
Rather than replacing auditors, AI is expected to transform the profession by creating a collaborative environment in which humans and intelligent technologies complement one another.
AI will increasingly perform tasks that involve processing massive datasets, identifying anomalies, recognizing patterns, and automating repetitive procedures.
Meanwhile, auditors will continue to provide the capabilities that technology cannot replicate: interpreting findings, exercising professional judgment, evaluating audit evidence, communicating with management and audit committees, and making informed decisions based on both quantitative analysis and professional experience.
The organizations most likely to succeed will be those capable of achieving the right balance, embracing technological innovation without becoming overly dependent on it, while avoiding exclusive reliance on traditional audit methods that are no longer sufficient in today’s rapidly evolving business environment.
Conclusion
Does artificial intelligence increase the risk of material misstatement?
The answer is yes, but not because AI itself is inherently risky.
The real risk arises when AI is implemented without mature governance, effective oversight, reliable data, or appropriate internal controls.
Conversely, when deployed within a comprehensive framework of governance, risk management, internal controls, and professional auditing practices, AI can become one of the most powerful tools available for reducing the risk of material misstatement.
The greatest threat does not lie in the technology itself, but in the mistaken belief that technology can replace human decision-making.
Ultimately, the quality of financial reporting and audit outcomes will continue to depend on an organization’s ability to balance the analytical power of artificial intelligence with the expertise, judgment, and accountability of experienced professionals.
Technology can process data. It cannot assume professional responsibility. It cannot exercise professional judgment.
And it cannot replace the trust that has been the cornerstone of the auditing profession for decades.
