Artificial intelligence is rapidly reshaping actuarial work. Tasks that once relied heavily on manual calculation and expert judgment, such as pricing, reserving and risk assessment, can now be supported by machine learning models capable of analyzing vast datasets within seconds. AI promises greater precision, efficiency and insight, allowing actuaries to automate routine processes and focus on higher-level decision-making. However, this emerging power brings a new challenge: ensuring that these systems reflect not just technical accuracy, but societal values.

​

Recent incidents in insurance and finance have shown that even sophisticated algorithms can produce unfair or discriminatory outcomes. Investigations in the UK found potential ethnic biases within pricing algorithms, while the EU’s AI Act signals a global shift toward enforceable ethical requirements. These developments highlight a critical point: advanced models alone are not enough. Without proper oversight, AI may amplify historical inequalities, damage public trust and expose organizations to regulatory scrutiny. As professionals trained to manage risk and uncertainty, actuaries are uniquely positioned and increasingly expected to address these concerns. 

​

As AI becomes more embedded in actuarial processes, the profession faces a growing need for a structured ethical approach. One useful framework is the ethical AI lifecycle, which maps six key stages: problem definition, data collection, exploratory analysis, modelling, evaluation and deployment. Each stage introduces distinct ethical risks, reminding actuaries that fairness and accountability must be embedded long before a model is switched on. For example, even seemingly harmless features such as postcodes or occupation can act as proxies for protected attributes, raising the risk of indirect discrimination. This means ethical scrutiny cannot be limited to model outputs; it must start from the moment the problem is framed and the data is selected.

​

This lifecycle aligns closely with the traditional actuarial control cycle, while extending its scope by addressing fairness, contestability and social impact, which are not always explicit in existing governance frameworks. In modelling, actuaries must balance predictive accuracy with with model transparency and interpretability, as well as regulatory expectations. In the evaluation stage, performance must be assessed not only by statistical fit but by robustness, transparency and fairness across subgroups. During deployment, models require continuous monitoring, effective appeals channels for customers, and structured feedback loops to catch emerging ethical concerns. These additions demonstrate that ethical practice is not a barrier, but a necessary safeguard in a world where automated decisions carry real human consequences.

​

The technical side of AI can also make ethical oversight challenging. Many modern pricing or reserving models use neural networks, which can be very powerful but difficult to interpret. To address this, actuaries are developing ways to make these models easier to understand and more reliable. For example, tools can be used to show which factors influence a prediction, while newer model designs are built to be more transparent from the start. There are also techniques to reduce bias in pricing models and to ensure results remain fair across different groups. When data is limited, actuaries can use methods that borrow insights from similar lines of business. And because AI models can sometimes give slightly different answers each time they’re trained, actuaries use techniques that combine multiple versions to make results more stable. All these approaches show that AI can still meet professional and ethical standards, as long as actuaries design and monitor the models carefully.

​

However, technology alone is insufficient. Professional standards in the UK, South Africa and other jurisdictions increasingly emphasize model understanding, transparency, and ethical accountability. Actuaries must be able not only to run complex models, but to justify their assumptions, document limitations and communicate risks clearly to both technical and non-technical stakeholders. As AI systems become more autonomous, the question of responsibility becomes sharper: who is accountable when an algorithm makes an error? The actuary? The developer? The organization? Ethical governance ensures that accountability remains clear and grounded in professional judgment.

​

For actuarial students, these developments signal a future that blends technical skill with ethical leadership. Learning machine learning tools is increasingly important, but so is understanding privacy, fairness, regulation and human behavior. The actuary of the future will be an AI-enhanced professional who collaborates across disciplines, understands how to adapt models responsibly and ensures that AI systems remain aligned with societal values.

​

AI is reshaping actuarial practice, but it does not diminish the profession’s relevance. Instead, it expands the scope of actuarial responsibility, positioning actuaries as critical guardians of fairness and trust in an increasingly automated world. By integrating ethical principles into every stage of the modelling lifecycle, actuaries can help ensure that AI serves not only efficiency and accuracy, but also the public interest. The future of the profession depends not just on what actuaries can model, but on how thoughtfully they guide the systems that now shape financial decisions.

References:
https://actuaries.org/app/uploads/2025/05/4_RON_Embracing-AI-Transforming-Profession_AI_Summit_Day1.pdf 

https://www.theactuary.com/features/2025/06/25/check-your-ai-framework-its-use-actuarial-practice