Fraud has always been a moving target for organizations, but with the acceleration of digital transformation it is evolving faster than ever before. In the article “Internal audit’s role in AI fraud detection” on Wolters Kluwer, the author argues that traditional rule-based detection systems are no longer sufficient to protect organizations from increasingly complex fraud schemes. Internal audit, the author emphasizes, must become a central player in guiding the responsible use of artificial intelligence for fraud detection and prevention.
AI is transforming the way organizations approach fraud detection because it can learn from vast and diverse datasets and adapt to changing conditions in real time. Whereas traditional controls focus on static rules that fraudsters can eventually learn to circumvent, AI systems apply machine learning and anomaly detection techniques to spot irregularities in transactions, patterns of behavior, or external signals that may indicate fraudulent activity. This adaptive quality allows organizations to stay one step ahead of increasingly sophisticated actors. For internal auditors, this creates both an opportunity and a challenge: they can leverage these technologies to enhance assurance, but they also need to evaluate whether AI tools are designed, implemented, and monitored responsibly.
The author highlights that AI-enabled fraud detection offers several important advantages. Models can deliver real-time insights, allowing earlier detection of suspicious activity and faster responses by management. They also promise greater accuracy, since machine learning systems are able to filter out noise and reduce false positives, which in turn frees compliance teams and auditors to focus on higher-value tasks. Another important benefit is scalability: AI systems can analyze millions of records across multiple geographies and business lines, something that manual approaches or static tools cannot achieve. Over time, these models improve as they are retrained with new data, enabling them to adapt continuously to evolving fraud tactics. Moreover, the data trails and documentation they generate can strengthen regulatory compliance and provide auditors with clearer evidence for their assessments.
At the same time, the author stresses that the adoption of AI in fraud detection is not without risks. If training data is incomplete, biased, or of poor quality, the outputs of the models may be skewed and lead to discriminatory or misleading results. Striking the right balance between false positives and false negatives is another major challenge, because both extremes undermine trust in the system. Privacy and data security are equally pressing issues, given that AI solutions often process highly sensitive financial and personal information. The threat landscape also includes adversarial attacks, such as attempts to manipulate or “poison” datasets in order to mislead AI models. In addition to these technical concerns, ethical and governance questions must be addressed: internal auditors and boards alike need to understand how models make decisions and ensure that these decisions remain transparent and accountable.
To mitigate these risks, the author suggests that internal audit should take an active and strategic role in guiding AI adoption. Clear objectives must be defined, and fraud detection models should be aligned with broader organizational risk strategies. Data quality and governance are foundational, as poor data will undermine even the most sophisticated model. A hybrid approach, where AI tools are complemented by human judgment and traditional audit techniques, can provide more robust results and maintain a necessary level of skepticism. Internal auditors should push for explainability so that AI-driven decisions can be interpreted and questioned when necessary, avoiding the “black box” problem. Continuous monitoring, validation, and retraining are also critical to ensure that models remain relevant as fraud patterns shift. Crucially, internal audit should be involved early in the design and implementation process of AI tools, so that oversight and ethical considerations are integrated from the start rather than treated as an afterthought.
The broader implication of the article is that internal audit is not simply a passive recipient of new technology but an active shaper of how it is used. By combining their expertise in governance, risk management, and controls with a forward-looking view of AI capabilities, internal auditors can help organizations strike the right balance between innovation and responsibility. They can provide assurance to boards and regulators that fraud detection systems are both effective and trustworthy, while also advising management on how to integrate AI into broader risk frameworks.
The author concludes that the rise of AI in fraud detection represents both a transformative opportunity and a serious test for the profession. Internal auditors who take on this challenge proactively will help their organizations stay ahead of fraudsters, strengthen control environments, and maintain stakeholder confidence in an era where trust is fragile and risk is ever more complex. The message is clear: internal audit must not only embrace AI but also guide its ethical and effective use.
The article “Internal audit’s role in AI fraud detection” was published on Wolters Kluwer and can be accessed here.