Managing general agents (MGAs) have built their position in the insurance market around agility, using specialist knowledge to identify opportunities, enter niche markets, respond to emerging risks and bring products to market faster than traditional insurers.
However, Earnix argues that maintaining this advantage is becoming more difficult as MGAs and their underwriters face increasing data volumes, faster-changing risks, greater transparency demands from capacity providers and rising regulatory expectations.
In a recent article, Andy How, director of insurance for the UK and Europe at Earnix, highlights the limitations of generic AI tools within insurance. While these solutions can generate content and automate simple tasks, How argues they were not designed to support the complexity of insurance decision-making.
The article suggests the missing element is insurance-native AI: technology built specifically around insurance decisions, including risk, pricing, underwriting, governance and compliance, while operating across existing insurance systems.
According to How, the next stage of AI adoption for MGAs will not be focused on simply adding more tools, but on using technology to support the decisions that underpin their competitive advantage.
Insurance decisions span areas including quotes, referrals, renewals, fraud assessments and claims outcomes, with each requiring consideration of profitability, customer experience, compliance and risk appetite.
How argues that generic AI can understand language but does not understand areas such as delegated authority, underwriting philosophy, appetite management or regulatory accountability.
The article positions Earnix’s AIOS launch as an example of an insurance-native approach. AIOS introduces an orchestration layer designed to connect models, AI agents, workflows, governance and human expertise around the decisions that drive performance.
Rather than replacing existing platforms, the article states that AIOS is designed to work alongside existing insurance systems, allowing intelligence to move across areas including pricing, underwriting, distribution and claims while maintaining governance and explainability.
How concludes that speed alone does not create agility. Applying the right AI at the right decision point, with the right level of human oversight, will be important as MGAs look to combine specialist expertise with insurance-native AI.
For more, read the full story here.
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