The insurance industry's predicament
Insurance may be similar to other industries in the fact that it is exploring and actively deploying AI, but where it differs is regulation.
Insurance is necessarily a highly regulated industry and claims processing and decision making cannot just simply be disrupted by technology. Each decision affects customer outcomes directly and must operate within strict expectations around fairness and transparency.
By its very nature, AI is probabilistic and this flies in the face of the principles that manage claims decision making. AI identifies patterns, generates outputs and makes predictions based on statistical inference. That is great in areas such as fraud detection and data extraction, but regulated claims decisions require something more rigid. Firms must be able show and explain exactly how and why a decision was reached. Regulators will not accept "our AI decided" as a sufficient explanation, nor should customers.
Claims decisions are among the most sensitive moments in the insurance workflow. They need to be consistent, transparent and capable of being mapped back to explicit rules and policy terms. This is why 39% of the industry say that transparent algorithms and decision logs would help reassure them about the use of AI in insurance.
The issue facing insurance firms is whether they can deploy AI within these regulated processes without creating unacceptable operational, reputational or compliance risks.
However, this doesn't mean that AI has no place in claims. Used properly, AI can extract structured data from unstructured sources, detect anomalies and flag potentially fraudulent claims, enrich claims data with external sources and prioritise cases for human or automated rules-based assessment.
But when it comes to claims decisions, the only compliant way to leverage AI is to use a rules engine. Because these are fully configured and controlled by the insurer, rules engines remove the unpredictability of machine learning models and instead apply deterministic, auditable business logic to every claim.
As a result, each decision is documented against explicit rules. This ensures transparency, compliance and reinforces customer trust in the fairness of the insurer and the industry.
The moment AI moves from assistant to judge, firms risk crossing a line that governance frameworks are not yet ready to support.
Keeping good governance at the heart of AI deployment
While the industry is keen to advance the use of AI, it's clear that compliance teams do have genuine concerns. This is driving a focus on how to make AI adoption viable in practice, which is showing up in procurement decisions.
Insurers are willing to compromise on cost in order to find the right AI solutions, prioritizing ease of integration and strong vendor support. In fact just 10% of senior professionals said cost would strongly influence their decision.
This is the sign of a necessarily cautious market. For all the noise around AI, insurers are becoming more discerning. They are not just asking only what a system can automate but whether it can be trusted in a regulated setting, whether it can integrate with existing workflows and whether it gives them enough visibility and control to stand behind the outcomes it produces.
In practice, this means combining AI with deterministic rules, strong oversight, clear escalation paths and audit-ready decisioning. It means using AI to improve speed and efficiency without surrendering control over outcomes that need to remain consistent and accountable.
Insurers have told us that they're expecting their full end to end claims administration to be handled by AI within the next 15 months on average. Those that embed good governance at the heart of AI deployment will be best equipped to accelerate adoption, stay competitive and remain compliant.
