What is model risk management for auditors and why should internal audit care now?

Model risk management for auditors refers to the set of practices, controls, and governance arrangements that help an organization identify, assess, monitor, and mitigate risks arising from the development, deployment, and ongoing use of models, including AI and statistical models, in financial reporting, decision-making, and audit processes. In the context of internal audit, model risk management is becoming central because boards and regulators expect assurance that models used to drive conclusions, allocate capital, or meet compliance obligations are reliable, accurate, and free from material bias or failure. For financial audit and broader enterprise risk management, weak model risk management can lead to misstatements, poor strategic choices, regulatory scrutiny, and loss of stakeholder confidence, which is why internal audit must embed model risk considerations into its annual risk assessments, audit plans, and assurance engagements. This is especially important as organizations rely more heavily on AI, advanced analytics, and third-party model providers to process data and support judgments, and as standards such as those from ISACA and emerging frameworks like AAISM provide guidance for IT auditors and risk practitioners on how to address AI and model-related risks systematically. Internal audit’s role in strengthening AI governance, as highlighted by Deloitte, and the increasing synergy between internal audit and risk management, as noted by CPA Practice Advisor, reflect a broader expectation that audit functions evolve from traditional financial checks to include assurance over data, algorithms, and the models that underpin key decisions. By understanding model risk management, internal audit can provide strategic intelligence on financial risk, validate the integrity of automated decision processes, and help management and the audit committee oversee a model-driven environment responsibly. This matters because the consequences of model failures can be severe, ranging from misreported financials to operational disruptions, and because the technology landscape is changing rapidly with General-Purpose AI Code of Practice expectations around cyber and physical security of models, TPRM governance for third-party risks, and new digital audit talent influencing how effectively audit teams can detect risk in digitized environments. What this means in practice is that internal audit should not wait for a failure before engaging with model risk management, but should build capabilities, collaborate with risk and technology functions, and integrate model risk considerations into their recurring audit and assurance activities in a structured and evidence-based way.

The how and why of model risk management for auditors starts with recognizing that models can fail in many ways, such as through data quality issues, specification errors, instability over time, poor interpretability, or inappropriate use outside their intended scope, and that these failures can directly affect the accuracy of financial statements and the reliability of audit evidence. From a practical standpoint, internal audit should first understand the organization’s model inventory, including models used in financial reporting, forecasting, credit decisions, or compliance, and map these to relevant processes, data sources, and third-party dependencies, drawing on insights from sources like Sia Partners’ work on financial risk management and assurance to strategic intelligence and Crowe’s identification of key risk areas for internal audit in financial services. Next, it should assess the maturity of model risk governance, covering roles and responsibilities, policies and standards, model development and validation practices, change management, and monitoring, while also evaluating controls around data lineage, model documentation, performance tracking, back-testing, and the management of model drift or degradation once models are in production. Because models are often built by data science teams and deployed by technology and business units, internal audit needs to coordinate with risk management and technology assurance colleagues, apply professional skepticism, and design audit procedures that test both the technical soundness of models and the effectiveness of governance and control processes, rather than attempting to replace model validation specialists or act as an internal model development function. Common mistakes to watch for include treating model risk as purely a technology issue, focusing only on complex AI models while neglecting simpler but critical statistical or spreadsheet-based models, relying on generic controls without sufficient testing of model-specific risks, and failing to challenge assumptions behind model inputs, assumptions, and outputs, which can lead to overstated confidence in results that appear precise but are materially flawed. Internal audit should also be cautious about over-reliance on vendor claims, ensure appropriate attention to third-party model risk and data integrity, and avoid situations where audit conclusions are based on model outputs without sufficient understanding of model limitations, calibration, and historical performance under different conditions. When model-related anomalies, governance gaps, or control weaknesses are identified, internal audit should escalate promptly to the audit committee and senior management, recommend concrete remediation actions with clear ownership and timelines, and track follow-up to ensure that risks are reduced to an acceptable level and that lessons are captured for future audits, thereby strengthening the overall control environment and supporting more informed decision-making across the enterprise.

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To integrate model risk management effectively into audit practice, internal audit teams can take a series of practical steps, beginning with building awareness and capability through training, collaboration with risk, technology, and data colleagues, and by leveraging frameworks and guidance such as the ISACA AI risk and assurance work, the AAISM standard introduced in 2025 for experienced, certified IT auditors, and insights from thought leaders on risk management decision-making for the enterprise. Next, they should work with management to develop or refine a model inventory and risk taxonomy, define clear ownership and accountability for model risk, establish or review policies on model development, validation, monitoring, and incident response, and ensure that key model risk indicators and performance metrics are defined and monitored in a way that links to business and audit objectives. Audit planning should include regular reviews of model risk as part of the annual risk assessment, targeted assurance engagements over critical models, and the use of data analytics and sampling techniques to test model inputs, outputs, and processes, while also paying attention to third-party model risk, data lineage, change management, and the adequacy of documentation and back-testing practices. Common pitfalls to avoid include proceeding without a clear understanding of the business context and model purpose, overcomplicating audit procedures to the point where they are not sustainable, and failing to communicate findings and recommendations in language that resonates with both technical and non-technical stakeholders, including the audit committee. Internal audit should also guard against the mistake of treating model risk as a one-time project, instead embedding it into ongoing assurance processes, using lessons from prior audits to refine approaches, staying current with emerging risks in AI and model use, and adjusting strategies as digital audit talent and tools evolve, as discussed in the Nature article on the impact of digital audit talent on audit digitization and detection risk. By combining strong governance understanding, practical testing, and thoughtful use of technology, internal audit can provide credible assurance that model risk is being managed appropriately and that the organization’s use of models supports sound financial reporting and strategic objectives.

In considering when to act or escalate around model risk, internal audit should look for signals such as significant model failures, material misstatements linked to model outputs, governance gaps, inconsistent monitoring, or increasing complexity in models without commensurate controls, as highlighted by trends in financial services risk areas and the growing importance of AI governance mentioned by Deloitte in the context of strengthening AI governance. When model-related issues are identified, timely escalation to the audit committee and management, supported by clear evidence and recommended remediation steps, helps ensure that risks are addressed before they escalate into larger problems, and reinforces the value of internal audit as a strategic partner in managing model and AI risk. It is also important to recognize when model risk intersects with other critical areas such as third-party risk management, data integrity, cybersecurity, and regulatory compliance, and to coordinate with risk, technology, and legal colleagues, as emphasized in KPMG’s TPRM governance discussion and the focus on third-party risks, to ensure consistent and effective responses. Because the model risk landscape is evolving with advances in AI, changes in regulation, and increasing reliance on external models and data, internal audit should maintain an ongoing dialogue with stakeholders, revisit its risk assessments and audit plans regularly, and adapt its assurance and advisory activities to emerging risks and best practices. By doing so, internal audit not only helps protect the integrity of financial reporting and decision-making, but also builds trust with regulators, management, and investors, demonstrating that the organization is proactively managing model-related risks in a structured, transparent, and evidence-based manner.

Taken together, model risk management for auditors is about providing reasonable assurance that models used within the enterprise are identified, governed, and monitored in a way that supports accurate financial reporting, sound decision-making, and compliance with applicable laws, regulations, and internal policies, while acknowledging the limitations and uncertainties inherent in model-based approaches. For internal audit, this means integrating model risk considerations into the core of audit planning, testing, and reporting, leveraging frameworks, guidance, and collaboration with risk and technology functions, and maintaining a healthy skepticism toward model outputs, documentation, and assumptions. By focusing on practical steps, learning from examples and standards such as those from ISACA, Sia Partners, Crowe, and KPMG, and avoiding common mistakes like siloed thinking or overreliance on model complexity, internal audit can strengthen its role in AI governance and enterprise risk management and deliver more meaningful assurance to the audit committee and broader organization. As the use of models and AI continues to grow, ongoing professional development, thoughtful use of digital audit tools, and a commitment to evidence-based, objective assessment will be essential for auditors who want to remain effective and credible in a model-driven world, and to support long-term resilience and trust in the organization’s financial and strategic decision-making.