
From Principle to Practice: Governing AI in General Insurance
Artificial intelligence is already being used across general insurance, from underwriting and pricing to claims and complaints triage, fraud detection, vulnerability identification, data analysis and customer communications. The governance challenge is not simply whether an AI system works. It is whether the system produces appropriate, defensible and consistent outcomes throughout its lifecycle. Four principles provide a practical foundation: Fairness, Adaptability, Explainability and Reliability. 1. Fairness Fairness means that any of the outputs generated by AI should be equitable and free from bias. It should not discriminate against any communities, groups, or individuals. Fairness can sometimes be a trade-off between procedural fairness (ensuring the system or process treats all people the same) and representational fairness (ensuring inequalities in the real world or past data, e.g. gender bias, aren’t perpetuated). [AI Fluency for directors, The University of Sydney, Sydney Executive Plus+ 2026) In general insurance, this is particularly relevant where AI is used for: risk selection, pricing or policy renewal; claims triage and fraud detection; identifying potentially vulnerable customers; prioritising complaints; or recommending whether a claim should be accepted (in full or in part), investigated or declined. Fairness does not necessarily mean treating every customer identically. Insurance legitimately differentiates between the type of consumer and general insurance product. The question is whether the differentiation is supported by relevant information, is legally permissible and does not operate as an inappropriate proxy for a vulnerability factor. Controls should include testing datasets for bias, comparing outcomes across customer cohorts, reviewing false-positive and false-negative rates, and requiring human review before an adverse or high-impact decision is made. 2. Adaptability Adaptability in an AI system refers to the ability to adjust to new information that falls outside its training data. A machine learning system that has learned to identify patterns in training data can be extremely efficient at predicting those patterns in the future. But they often struggle to cope with disruption and change, when the world changes and no longer matches its training data. (ibid, AI Fluency for directors) Adaptability requires: monitoring for model drift and unexpected changes in outcomes; reassessment when the model, data, intended use or regulatory (or Code) environment changes; defined triggers for escalation, suspension or human intervention; change-management controls over vendor and internal updates; and a process for retiring AI systems that are no longer suitable. Governance should therefore continue after implementation. Approval at the commencement of an AI project is not enough. 3. Explainability AI should be transparent and explainable. Transparency is about how much of your system’s inner workings you can understand. Explainability means how and why certain decisions are made can be understood by the people who interact with the AI, be they employees or customers. (ibid, AI Fluency for directors) This is critical where AI contributes to: premium or policy coverage decisions; the identification of suspected fraud; claim delays, part settlements or denials; complaint outcomes; or customer facing advice or communications. Explainability does not require every employee to understand the technical construction of a model. It does require





