Mieke Jans

Professor

Prof. Dr. Mieke Jans is a Professor at the Faculty of Business Economics, Hasselt University, and an Associate Professor at Maastricht University. She is affiliated with the Business Informatics research group and the Digital Future Lab at UHasselt. Her research focuses on process mining, audit analytics, and the application of data-driven techniques to improve assurance and internal control. She has published extensively on topics such as continuous auditing, fraud detection, and process deviation analysis.Mieke supervises multiple Ph.D. projects in areas like process mining for auditing, behavioral analytics, and healthcare process analytics. She teaches courses in Business Informatics and Quantitative Methods and actively contributes to bridging academia and practice through collaborations and fellowships. Her work has appeared in leading journals and conferences, including The Accounting Review, International Journal of Accounting Information Systems, and IEEE ICPM.

Auditor reliance on internal controls: challenges, insights, and the future of data analytics
Auditors have long grappled with how much they can trust a client’s internal control system. Strong internal controls can make audits more efficient, but what defines “high quality” controls remains unclear and many companies still fall short. Research shows that internal control quality depends heavily on firm-specific factors like size, complexity, governance, and risk profile. While robust controls improve financial reporting, reduce operational risk, and even lower audit fees, a significant number of firms continue to exhibit material weaknesses, raising concerns about audit reliability.
For auditors, evaluating internal controls is now a mandatory part of the process under standards like Sarbanes-Oxley Section 404 and ISA 315. Yet, this evaluation is challenging: controls vary widely across organizations, and clear guidelines are lacking. Auditor independence and a deep understanding of the client’s operations are critical, but experience alone doesn’t guarantee better assessments. Looking ahead, technology promises to reshape this landscape. Data analytics and AI can help auditors test entire populations of transactions, flag anomalies, and reduce reliance on client controls. Continuous auditing and machine learning may soon make audits faster, more comprehensive, and less dependent on traditional control systems, though these benefits will mostly apply to large-scale clients. In short, internal controls remain central to audit quality, but their evaluation is complex and context-driven. As technology advances auditors must adapt and balancing traditional judgment with innovative tools to ensure trust and transparency in financial reporting.
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When Do Auditors Rely on (Bad) AI Advice?
Generative AI systems (“GenAI”) can provide auditors with natural-language recommendations that resemble professional advice. Such tools have the potential to support audit judgments. However, a lack of transparency in their processes and reasoning also raises practical questions: when, and to what extent, should auditors rely on AI-generated advice? Because GenAI recommendations are not directly explainable, auditors must rely on indirect cues to assess credibility. In practice, a key indirect cue is AI performance. Firms and software providers commonly disclose stated accuracy levels, either framed in terms of accuracy (“the AI system is 95% accurate”) or in terms of error (“the AI system has a 5% error rate”). Framing AI performance in terms of either “accuracy” or “error” may affect auditors’ reliance in unanticipated ways. The key issue for audit practice is whether these performance cues support appropriate calibration. That is, auditors should use sound advice but remain skeptical of weak output. In this practitioner report, we summarize evidence from an experimental study with practicing auditors examining the effects of stated accuracy and performance framing on reliance on high-quality and low-quality GenAI advice. Our findings show that performance communication influences reliance decisions, with implications for the design and implementation of GenAI in judgment-intensive audit tasks.
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Understanding Auditors’ Reliance on Emerging Audit Technologies
Audit firms are rapidly integrating Generative AI (GenAI) into their workflows. While these tools can enhance efficiency and support complex judgments, the key challenge is not whether AI provides useful input, but whether auditors use it appropriately. The literature shows that auditors’ reliance on AI is shaped more by behavioral responses, system design, and organizational context than by the underlying technology. Three insights emerge. First, auditors face a calibration problem. They may under-rely on AI due to algorithm aversion, discounting AI-based evidence, relative to human experts, even when it is equally reliable. At the same time, they may over-rely on AI when outputs appear authoritative, fluent, or easy to use. Both problems impair audit quality: under-reliance biases judgments toward management, while over-reliance reduces professional skepticism. Second, reliance depends critically on how AI is designed and embedded in the audit process. Features such as perceived control (e.g., the ability to provide input), adaptability of algorithms, and task–technology-fit influence whether auditors trust and use AI outputs. AI is more effective when it aligns with task uncertainty and complexity, and when auditors can meaningfully engage with the system. Poorly designed or poorly communicated tools risk being ignored or misused. Third, AI affects not only decisions but also how auditors think about decisions. GenAI can improve understanding of complex evidence and help auditors better identify when to raise issues, particularly in remote settings. However, AI can also inflate confidence while reducing self-monitoring, making auditors less aware of when they may be wrong. This creates a risk of overconfidence and inappropriate reliance. Overall, the literature highlights that successful AI adoption is also a behavioral and organizational challenge, not just a technological one. To realize the benefits of AI, audit firms should consider three key levers. First, governance: providing clear guidance on when and how AI should be used and evaluated. Second, design and communication: ensuring that tools align with task demands and enable auditors to meaningfully engage with the system. Third, training and oversight: developing auditors’ ability to critically assess AI outputs and appropriately calibrate their reliance.
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Internal Controls
The importance of internal control over financial reporting (ICOFR) has increased over the past few decades. All over the world, governments are reinforcing regulations related to internal controls, forcing firms as well as their auditors to direct more attention to the quality of internal controls in place. Along with the growing importance of ICOFR, researchers have conducted studies examining different aspects relating to internal controls. To provide a broad overview of the current understanding of internal control systems, this literature review provides a summary and synthesis of studies conducted.  
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Internal controls
The auditor’s reliance on client internal controls has always been a contentious issue. If clients have high quality internal controls, auditors should be able to rely on these controls, making the audit more efficient. However, what constitutes a “good” internal control system is unclear, which makes it difficult for the auditor to determine how to integrate a client’s internal control system into the auditing procedures. Research suggests that the quality of a client’s internal control system very much depends on the context of the firm, and no clear guidelines exist. This is worrisome, given that a high proportion of audit clients shows significant shortcomings in their internal control over financial reporting. Clear evaluation criteria are lacking and necessary, but the rise of data analytics is likely to partially solve this issue. Computer algorithms facilitate large-scale tests by the auditor and may flag suspicious transactions, reducing the need for the auditor to depend on their clients’ internal control systems.
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