Regulatory compliance considerations for AI-enabled actuarial services

In the next five years, we expect significant advancements in the insurance sector, driven by digital transformation and the adoption of advanced technologies like machine learning and artificial intelligence. The integration of these technologies offers huge potential to enhance pricing models, underwriting processes and customer engagement, but also brings major implications for the actuarial function.

Insurers will need to address important regulatory compliance considerations to ensure transparency, fairness and adherence to legal standards as the actuarial function becomes more AI-enabled. Partnering with technology providers may also be an important step that allows insurers to harness these cutting-edge capabilities without the extensive time and resources needed to develop them in-house.

The regulatory landscape

In the context of regulatory compliance, insurers must balance the need for sophisticated modeling practices with the requirements of transparency and fairness. Regulatory bodies have recognized the transformative power of AI and have issued guidance and formal legislation to support the responsible use of these technologies.

California and Colorado have been at the forefront of this regulatory activity, establishing specific requirements for AI model risk management, emphasizing the importance of addressing bias and preserving fairness in actuarial practices. In Canada, insurance regulation occurs primarily at the federal level. The Canadian regulator has issued guidance on AI model risk management, focusing on bias and fairness.

To navigate the regulatory landscape effectively, insurers must consider several key factors in their AI-enabled actuarial practices:

1. Data privacy and security

The use of AI and ML technologies in actuarial services involves the processing of vast amounts of data. Insurers must comply with data privacy and security regulations to protect sensitive customer information. This includes implementing robust data governance frameworks, encryption protocols and access controls to safeguard data throughout its lifecycle.

2. Technical capabilities and compliance

Insurers must assess whether their technical capabilities can comply with regulatory requirements. Early-stage AI in actuarial rate setting relied heavily on open-source solutions, which may not fully meet the stringent standards set by regulators. Partnering with technology providers can help insurers harness cutting-edge capabilities and comply with regulatory standards. This strategic collaboration provides a competitive edge by enabling rapid innovation and superior service offerings.

3. Transparency and explainability

One of the primary challenges in regulatory compliance is establishing transparency and explainability in AI models. Insurers must be able to demonstrate the core principles of their models, including how they achieve fairness and avoid bias. This requires robust documentation and auditing processes to ensure that AI models are transparent and understandable to both regulators and stakeholders.

4. Bias and fairness

Regulatory bodies have placed significant emphasis on addressing bias and maintaining fairness in AI-enabled actuarial services. Insurers must implement rigorous testing and validation procedures to identify and mitigate any potential biases in their models. This involves ongoing monitoring and adjustment of models to preserve fairness and avoid bias over time.

AI assistants: Accelerating actuarial workflows

One of the most promising developments in AI is the rise of AI assistants tailored specifically to actuarial workflows. These tools are not replacements for actuarial judgment; they are designed to enhance productivity, accuracy and insight generation.

AI assistants are already being used in several areas:

• Automated documentation and reporting: Generative AI tools can draft initial versions of memos, pricing justifications or regulatory filings, reducing time spent on documentation.
• Real-time model interpretation: AI assistants can flag anomalies, interpret model outputs and suggest refinements, improving model governance.
• Data triage and preprocessing: Assistants can clean and validate large datasets, enabling actuaries to focus on analysis and strategy.
• Knowledge retrieval: AI tools can surface relevant regulatory guidance, past assumptions and peer-reviewed methodologies from both internal and external sources.

As these tools become more embedded in actuarial platforms, insurers will need to implement them in a way that aligns with regulatory expectations around human oversight, fairness and transparency.

Taking action

To support regulatory compliance and harness the full potential of AI-enabled actuarial services, insurers should consider the following actions:

• Conduct a comprehensive assessment of technical capabilities to determine compliance with regulatory standards.
• Implement robust documentation and auditing processes to support transparency and explainability in AI models.
• Establish rigorous testing and validation procedures to identify and mitigate bias in AI models.
• Develop and maintain a robust data governance framework to ensure data privacy and security.
• Engage in proactive collaboration with regulatory bodies to stay ahead of evolving compliance requirements.

AI is reshaping actuarial science—offering a path to greater precision, efficiency and insight. But with this opportunity comes responsibility. Insurers that invest in strong governance, transparent practices and compliance-driven innovation will be best positioned to lead in an AI-enabled future.