How to audit AI models to comply with the European AI Law?

The European Union's new AI Act is poised to transform the regulatory landscape, particularly for the financial sector. For organizations using artificial intelligence models in critical areas such as credit assessment or fraud detection, auditing these systems is no longer a best practice but a mandatory legal requirement. Is your organization prepared to demonstrate the transparency, robustness, and fairness of its models to comply with the regulations?

Key Requirements of the AI Act for High-Risk Financial Systems

The AI Act classifies many systems used in finance, such as those for creditworthiness assessment or life and health insurance pricing, as 'high risk'. This means they must meet strict requirements before being launched and throughout their entire lifecycle. These obligations range from the governance of training data, ensuring its high quality and lack of discriminatory bias, to the creation of comprehensive technical documentation detailing the model's operation and purpose.

Furthermore, regulations require a high level of transparency, meaning that financial institutions must be able to explain how their AI models make decisions. This is crucial not only for regulators but also for maintaining customer trust. Effective human oversight is another fundamental pillar, ensuring that a person is always available to intervene and correct or override system decisions when necessary to mitigate risks.

The Audit Process: Fundamental Steps for Compliance

An effective AI audit for legal compliance is not simply a checklist. It begins with a risk assessment to identify and classify all AI systems in use according to their criticality. Next, a thorough audit of the datasets is conducted, verifying their provenance, quality, and representativeness to avoid bias. The following step is technical validation of the model, where its performance, accuracy, and robustness are tested against different scenarios and potential adversary attacks.

The final phase focuses on assessing fairness and explainability. Advanced techniques are used to detect and mitigate discriminatory biases based on gender, ethnicity, or other protected characteristics. Simultaneously, mechanisms are implemented to ensure that the model's decisions are interpretable by both internal teams and end clients. Meticulously documenting each step of this process is vital to demonstrating compliance to the relevant authorities.

Ultimately, auditing AI models under the new European law framework is not just a regulatory burden, but a strategic opportunity for the financial sector. Ensuring that systems are fair, transparent, and robust strengthens customer trust, mitigates reputational and operational risks, and positions the institution as a responsible leader in the AI era. At Codice AI, we help financial institutions navigate this complexity, ensuring their innovations comply with regulations and generate sustainable value.

Key Points of the Article

• The EU AI Act imposes strict requirements on 'high-risk' systems, common in the financial sector such as credit assessment and insurance pricing.
• A comprehensive audit should assess the quality of the data, the technical robustness of the model, the absence of bias, and the transparency of its decisions.
• Effective human oversight is an essential requirement to be able to intervene and correct automated decisions.
• Documenting the entire audit process and the model lifecycle is crucial to demonstrating regulatory compliance to the authorities.
• Proactively adapting to the AI Act not only avoids penalties, but also builds customer trust and provides a competitive advantage.