{"id":4337,"date":"2026-03-09T09:23:42","date_gmt":"2026-03-09T09:23:42","guid":{"rendered":"https:\/\/dev.opendesignsin.com\/insillion\/?p=4337"},"modified":"2026-03-09T09:52:14","modified_gmt":"2026-03-09T09:52:14","slug":"p-and-c-automation-insurance-software-mgas","status":"publish","type":"post","link":"https:\/\/dev.opendesignsin.com\/insillion\/blog\/p-and-c-automation-insurance-software-mgas","title":{"rendered":"P&C Automation: The Role of AI and Agentic AI in Insurance Automation Software"},"content":{"rendered":"
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\u00a0P<\/span><\/span>&C<\/span> Automation<\/span><\/span>\u00a0<\/span><\/h2>\n

Automation and AI in insurance are no longer emerging concepts, they are operational realities.\u00a0<\/span>In 2017, fewer than 50% of carriers reported using AI or machine learning in underwriting.<\/span>\u00a0By 2021, that figure had risen to 94%, according to benchmarking data from Luma Financial Technologies. The release of generative AI tools like ChatGPT accelerated this further, making advanced AI capabilities accessible across business functions almost overnight.<\/span>\u00a0<\/span><\/p>\n

The industry is now transitioning from non-agentic AI, bounded, task-specific tools, toward agentic AI, which executes entire end-to-end processes to deliver a business outcome. That shift carries significant implications for how MGAs are structured, how underwriting workflows are designed, and what operational readiness actually requires.<\/span>\u00a0<\/span><\/p>\n

MGAs are well-positioned to move faster than traditional carriers.<\/span>\u00a0The delegated authority market now\u00a0represents\u00a0roughly<\/span>\u00a0<\/span><\/b>40% to 60%<\/span><\/b>\u00a0of gross written premium in intermediated channels, depending on the source.\u00a0<\/span>In the US and other major markets, \"greenfield\" MGAs are launching with digital-first architectures and no legacy constraints,<\/span>\u00a0and the ability to build modular, API-driven ecosystems from day one<\/span>.<\/span>\u00a0<\/span><\/p>\n

At\u00a0the same time, consolidation continues across the MGA landscape. Mid-sized MGAs are being absorbed into larger platforms, while new niche MGAs are\u00a0emerging\u00a0with focused underwriting strategies and technology-enabled operating models. The common denominator is agility.<\/span>\u00a0<\/span>Yet alongside optimism, concerns around data privacy, model hallucinations, non-determinism, and AI governance are rising. As automation and AI in insurance expand, governance frameworks must evolve in parallel.<\/span>\u00a0<\/span><\/p>\n

Underwriting Challenges: Why AI Is Being Considered<\/span><\/b>\u00a0<\/span><\/h2>\n

The insurance industry is not yet in a golden age of underwriting, and P&C automation offers the clearest path toward one.<\/span>\u00a0According to a study by\u00a0<\/span>Boston Consulting Group<\/span><\/b><\/a>,<\/span><\/b>\u00a0specialty underwriters spend approximately 40% of their working day on administrative work and data entry.\u00a0<\/span>Broker submissions routinely run to 230 or 300 pages, driven in part by brokers sending everything they\u00a0have to\u00a0manage their own E&O exposure. Underwriters are then expected to read, structure, and re-key that information across multiple systems before making a pricing or coverage decision. The burden is not a shortage of\u00a0data,\u00a0it is the cost of ingesting and organizing it.<\/span>\u00a0<\/span><\/p>\n

The commercial impact is measurable.\u00a0<\/span>Research<\/span>\u00a0cited\u00a0by\u00a0<\/strong><\/span>Ernst & Young<\/strong><\/span><\/a>\u00a0suggests carriers are approximately 60% more likely to\u00a0write\u00a0a risk if they respond to a broker first.\u00a0Faster response times correlate directly with revenue outcomes.<\/span>\u00a0<\/span>This cultural dimension\u00a0remains\u00a0as important as\u00a0technology\u00a0itself.<\/span>\u00a0<\/span><\/p>\n

Where Automation and AI in Insurance<\/span><\/b>\u00a0Can Actually Help MGAs<\/span><\/b>\u00a0<\/span><\/h2>\n

AI adoption within MGAs is strongest in bounded, practical use cases, particularly in submission intake,\u00a0claims FNOL, and actuarial support.\u00a0Fully automated specialty underwriting\u00a0remains\u00a0some distance away.<\/span>\u00a0<\/span><\/p>\n

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  1. Submission Intake<\/span><\/b>\u00a0<\/span><\/li>\n<\/ol>\n

    Submission ingestion<\/a>\u00a0<\/span><\/b>is the clearest early win for P&C automation. An incoming request for\u00a0a quote\u00a0can pass through a workflow, call an intelligent document processing<\/a> API powered by an LLM, and return structured data into downstream systems, with a human reviewing flagged fields rather than the entire document.<\/span>\u00a0<\/span><\/p>\n

    LLMs perform well on structured extraction tasks, achieving<\/span>\u00a085% to 95% accuracy on clean documents\u00a0<\/span><\/b>such as ACORD forms and standardized loss runs. However, insurance tolerance for error on pricing, limits, retentions, and compliance-sensitive fields is near zero.\u00a0Two risks are worth noting:<\/span>\u00a0<\/span><\/p>\n