Annelies Renders

Professor

Annelies Renders is a Professor in the Department of Accounting and Operations Management at BI Norwegian Business School. She obtained her Ph.D. in Accounting, Finance, and Insurance from Katholieke Universiteit Leuven. Before joining BI in 2023, she held academic positions at Maastricht University and the Open University in Heerlen.Her research focuses on the unintended consequences of accounting rules and choices, including their impact on financial stability, disclosure practices, and corporate governance. She has received prestigious grants such as the NWO VENI, VIDI, and ASPASIA awards for her work on the interplay between accounting standards and macroeconomic outcomes. Her publications appear in leading journals, including The Accounting Review, Review of Accounting Studies, and Journal of Accounting and Economics.Annelies teaches financial accounting at undergraduate and graduate levels and supervises theses and Ph.D. research. Recently, her work has expanded to topics such as CSR and the effects of accounting regulation on employee well-being.

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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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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