Generative AI is making it faster and cheaper for insurers to develop their own technology, but Earnix warns that lower development barriers do not remove the long-term costs of governance, maintenance and regulatory accountability.

The InsurTech firm argues that insurers reassessing their technology strategies risk focusing too heavily on how quickly they can create a working solution, rather than what it takes to operate it reliably at scale. AI can generate code, accelerate model development and turn prototypes into applications in a fraction of the time, prompting insurers to reconsider whether buying established software still makes sense. However, Earnix says creating a model or calculating a rate is only part of the challenge. Insurers must also turn these capabilities into reliable, governed and scalable processes that support business decisions over the long term.

AI-assisted development has made it easier for insurers with strong actuarial, data science and technology teams to build their own software. Yet once an application enters a production environment, the organisation becomes responsible for its ongoing operation, including testing, security, governance, auditability, approval workflows, performance, regulatory requirements, maintenance and further development. These responsibilities continue long after launch, meaning that comparing the upfront cost of an internal build with the price of an external product provides an incomplete picture. Insurers must also consider the resources required to maintain, govern and improve internally developed systems over several years.

This also raises questions about how insurers use their specialist teams. Actuaries create value by assessing risk, evaluating assumptions, developing pricing strategies and responding to changing market conditions, while technology teams balance competing demands across the wider business.

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