The adoption of AI in captive operations is a rapidly evolving topic, with AI tools advancing at a rapid pace: a major development every six months, while adoption rates vary greatly not only from one industry to another, but also between competitors within the same industry. It is therefore useful to take stock of the adoption of AI by captives and their managers in the first quarter of 2026.

As a matter of fact, captive insurance programs are starting to harness artificial intelligence for data management, risk pricing, and claims processing, though adoption remains in early stages and requires careful human oversight.

AI’s most immediate impact is streamlining data management, where captives traditionally struggle with complex information flows from multiple internal systems and external vendors. The technology can format and interpret data in seconds compared to hours of human work, freeing professionals to focus on higher-value tasks.

In pricing and underwriting, AI is identifying unexpected correlations between risk variables to help assess vulnerabilities within captive portfolios. Large language models (LLMs) are also being used for fraud detection at the policy level, analyzing behavioral patterns in claims data more quickly than traditional methods. That said, the application of statistical methods in fraud detection requires that samples be sufficiently large; while this may be the case in high-frequency lines such as health insurance, it is less so in lines where severity dominates, which are frequently found in captives. And even then, policy-level data often is not available to the captive on a systematic basis, but only for audit or other ad-hoc purposes. Adjustments to the treaties, providing for monthly or quarterly detailed reporting. are required.

Emerging agentic AI applications could automate early claims lifecycle processes, including initial assessments and document verification, significantly reducing processing times. Additionally, AI tools are helping compile governance documentation and operational process explanations at speed. LLMs in particular, are used to monitor regulatory changes in the countries where the captive is present.

At this time, the biggest barrier isn’t technological but trust. Captive managers express concerns about data safety and output reliability, making human oversight essential throughout AI implementation. Organizations beginning with low-risk, high-volume tasks like data ingestion are building familiarity while positioning themselves to leverage rapidly advancing AI capabilities in more complex captive operations.

Beyond the captive’s management, the AI policies of the captive’s parent company and the attention paid to the subject by the captive’s board of directors play a major role in the pace of adoption and the mode of AI governance by the captive, which may explain a large part of the variations observed from one captive to another.