5 Key Strategies to Effectively Audit Model and AI Risk in Finance

In the modern financial landscape, mathematical models have become the central nervous system of decision-making. From underwriting credits and valuing exposures to detecting fraud and forecasting capital requirements, reliance on quantitative analysis is ubiquitous. However, this reliance introduces “model risk”—the potential for adverse consequences, financial loss, or reputational damage arising from decisions based on incorrect or misused model outputs. For a comprehensive overview, see our what is model risk and model risk management (MRM) in banking guide.

The requirement of internal audit teams has been shifted from mere compliance checks to a thorough and detailed assessment of the Model Risk Management (MRM) model. This is made more complicated by the direct implication of the aggregator of Artificial Intelligence (AI) and Machine Learning (ML), which brings in the “black box” complexities that the traditional audit methods can hardly tackle.

In this blog, we address how agile auditing could be applied to assure compliance with AI Model Risk Management architecture while addressing the specific risks the AI poses.

1. Defining the Audit Scope and Inventory

The audit of a financial system starts with the very question of whether the financial institution is aware of what models it possesses.

The guidelines lay down by the regulators like the Federal Reserve SR 11-7 and OCC 2011-12 define a model as a quantitative method that applies statistical, economic, financial, or mathematical theories to process input data and compute the quantifiable risk.

Therefore, a critical audit step is the verification of the inventory of the model of the organization and the inclusion of various quantitative methods that conform to the guidance even when termed as a “calculator” or “tool” to bypass governance.

Audit Action Item
The auditors are the ones who have to sift through the model inventory and scrutinize them for completeness. This includes verifying that the inventory properly delineates the model’s purpose, assigned risk level (tier), development stage, and previous validation results. Crucially, the inventory must track models throughout their lifecycle—from development to retirement. For deeper insights, see our top model risk management priorities for the banking industry blog.

2. The Three Lines of Defense

Effective MRM relies on a clear separation of duties, typically structured around three lines of defense. The first line develops and owns the models; the second line (risk management) validates them; and the third line (internal audit) provides independent assurance.

1. Governance and Policy Review
   Auditors must evaluate whether the Board of Directors and senior management provide active oversight. This includes reviewing board meeting minutes to ensure that model risk is discussed and that the board understands the aggregate model risk profile. Policies must be explicitly defined, covering model development, implementation, use, and validation.
2. Independence of the Second Line
   A critical audit objective is to verify the independence of the model validation function. Validators must not be responsible for model development or use, and they must have the authority to challenge developers effectively. The audit should assess whether validators have the technical competence and influence to delay model implementation if significant deficiencies are found.

3. Validating the Validator

Internal audit does not re-validate every model; rather, it “validates the validator” to ensure the MRM framework is functioning. This involves reviewing the validation process to ensure it includes three core elements.

1. Validators must review the model’s design, theory, and logic against published research and industry practice.
2. Auditors should verify that models are monitored to confirm they perform as intended over time, assessing whether changes in market conditions necessitate redevelopment.
3. Auditors should assess outcomes analysis through back-testing, comparing actual results against model forecasts to evaluate ongoing predictive accuracy and identify potential model drift.

To audit these processes effectively, auditors can utilize scoring mechanisms. Scorecards can evaluate whether the validation governance, policy, and processes are “fully evident” or lacking, providing a quantitative metric for the maturity of the MRM framework.

4. AI, ML, and the Limits of Traditional Validation

The integration of AI and ML models has transformed finance but has also rendered traditional validation techniques insufficient. Unlike linear regression models, AI models often function as “black boxes,” where the logic behind a decision is often opaque.

Below are some of the key challenges for auditors and model risk teams.

1. The Explainability Challenge
   Auditors must assess how the organization manages “explainability.” If an AI model denies a loan, can the institution explain why?
   Regulatory guidance increasingly emphasizes that AI decision-making processes must be reasonably understood by bank personnel. Auditors should look for the use of explainability frameworks (e.g., SHAP values) that translate model outputs into interpretable and defensible insights.
   Read more about how leading banks validate AI and ML models differently.

2. Model Drift and Continuous Monitoring
   AI models are highly susceptible to “model drift” or “decay,” where predictive performance deteriorates as real-world data diverges from training data. Traditional annual validation cycles are often too slow for AI. Auditors should verify that the organization employs continuous monitoring tools that trigger automated alerts when performance metrics (like false positive rates) breach predefined thresholds.
3. Bias and Ethical Considerations
   AI models can inadvertently learn and perpetuate biases present in historical data. An MRM audit must evaluate whether the validation process includes specific testing for algorithmic bias to ensure fairness and compliance with consumer protection laws.

5. Managing Third-Party and Vendor Risk

Many institutions rely on third-party vendor models, particularly for specialized tasks like Anti-Money Laundering (AML). However, relying on a vendor does not absolve the financial institution of responsibility.

Auditors must verify that the organization has performed its own validation of vendor models. Since vendors may not share proprietary code, the audit should look for “challenger testing”—where the bank compares the vendor model’s outputs against a simplified internal benchmark model to ensure results are reasonable. Additionally, the audit must ensure contingency plans are in place should a critical vendor model become unavailable.

For insights on AI-driven AML validation, see our generative AI transforming financial crime compliance post.

Leveraging Technology in the Audit

To keep pace with the volume and complexity of modern models, the audit function itself must evolve. Manual spreadsheets are no longer sufficient for tracking model risk. Auditors should look for a centralized system that stores all model documentation, risk assessments, and validation reports. This prevents version control issues and streamlines evidence collection. Advanced Governance, Risk, and Compliance (GRC) platforms can also help automate the tracking of validation findings and remediation timelines, ensuring that high-severity issues do not slip through the cracks,.

Conclusion

A well-executed MRM audit does more than satisfy regulatory requirements—it strengthens organizational resilience. By rigorously challenging the governance of both traditional and AI-driven models, internal audit enables institutions to innovate with confidence.

As the industry moves deeper into the AI era, the ability to validate the “black box” will become a critical competitive differentiator. Financial institutions that build these advanced audit frameworks will be able to mitigate financial and reputational risk and unlock data-driven strategic growth.

Anaptyss co-creates robust MRM and AI governance frameworks with financial institutions, combining structured audit methodologies with deep validation expertise.