Generative AI is challenging the traditional economics of insurance technology development, allowing insurers to create software faster and with fewer resources. However, Earnix warns that the apparent savings of building in-house can obscure the long-term costs of maintaining, governing and scaling those systems.
For insurers weighing up whether to develop their own technology or purchase established platforms, AI has shifted the balance. Code can be generated in seconds, models can be tested more efficiently and prototypes can become working applications far more quickly than before. Yet the InsurTech company argues that faster development does not necessarily translate into cheaper or simpler technology ownership.
The real challenge begins once a solution moves into production. Insurers must ensure their systems remain secure, auditable and compliant, while managing testing, approval processes, performance and ongoing maintenance. According to Earnix, the effort required to build an application is only one part of the equation, with the cost of operating and improving it over time often overlooked.
That distinction has implications for how insurance businesses allocate their technical expertise. Actuaries and data scientists are responsible for assessing risk, developing pricing strategies and responding to changing market conditions, while technology teams must support priorities across the wider organisation. If these specialists also spend significant time maintaining internally developed software, conducting quality assurance and updating controls, fewer resources may be available for work that directly advances the business.
The problem can intensify as teams change and development priorities shift. Original developers may move into different roles, budgets may be redirected and systems that once received considerable attention can become harder to maintain. Earnix argues that insurers should therefore assess not just whether their teams can build a solution, but whether developing and operating it internally is the most effective use of their expertise.
AI also does not remove insurers’ responsibility to demonstrate that their technology works as intended. Even as automated development and testing tools become more sophisticated, firms must be able to explain how models and processes were created, which changes were approved, what testing took place and how decisions can be reconstructed. Whether a system is purchased, developed internally or created with AI assistance, accountability for its use remains with the insurer.
As AI becomes more embedded in insurance operations, governance must evolve alongside it. Systems need to remain reliable as regulations, market conditions, models and business requirements change. Earnix argues that insurers must be able to demonstrate ongoing performance and control, rather than relying on successful initial testing as evidence that a solution will remain fit for purpose.
The debate also extends to customisation. Insurers have different products, risk appetites, pricing strategies and workflows, making internal development an attractive option for businesses seeking to differentiate themselves. However, Earnix argues that bespoke requirements do not mean every component must be built from scratch. Established platforms can provide the underlying infrastructure for modelling, pricing, rating, deployment and governance while allowing insurers to retain control over their own data, models and business logic.
This creates an alternative to treating build and buy as an either-or decision. Insurers can develop capabilities where they offer a genuine competitive advantage while relying on established technology for functions that do not need to be recreated internally. As AI lowers the barriers to software development, identifying where proprietary technology adds value becomes increasingly important.
Earnix, which has spent more than 25 years supporting the infrastructure behind high-stakes insurance decisions, encourages insurers to consider the full lifecycle of a technology investment. This includes who will maintain a system in three to five years, how it will respond to regulatory changes, whether updates can be tested and governed effectively, and how much internal capacity will be required as usage grows.
AI may make building insurance technology more accessible, but it does not remove the complexity of running it safely and consistently at scale. For insurers reconsidering their technology strategies, the critical decision is how to balance the flexibility of internal development with the governance, reliability and long-term support offered by established platforms.
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