As generative AI continues to transform industries, one thing has become clear: insurance is different.
While ChatGPT, Gemini, and Claude can generate content and answer questions with impressive fluency, they weren’t built to automate insurance submissions, reconcile bordereaux files, or triage claims.
The needs of carriers, MGAs, and reinsurers go beyond text generation. They require accuracy, compliance, auditability, and deep operational context.
Generic AI introduces too much risk and too little return. The future of AI in insurance lies with insurance-native AI solutions designed to understand the language, logic, and nuance behind every policy, claim, and decision.
Insurance-native AI refers to artificial intelligence tools and agentic systems built specifically for the insurance industry. Unlike general-purpose AI trained on broad datasets, these systems are fine-tuned with insurance-specific context, using verified data, workflows, and domain rules to guide outputs.
They operate within the real-world constraints, terminology, and decision paths of insurance operations. From underwriting intake to bordereaux reconciliation, insurance-native AI is built to deliver accuracy, auditability, and efficiency at scale, without using your data to train LLMs.
Most AI platforms are trained on open web data. But insurance operates in a world of PDFs, spreadsheets, bordereaux, and specific regulatory structures. The terminology and workflows are unique.
Insurance-native AI is tuned with the contextual understanding of real insurance operations— not just to understand terms like “cedent,” “SOV,” or “submission binder,” but to know what they mean in context and how they influence decisions. It recognizes the difference between facultative and treaty reinsurance. It understands how a single clause in a binder affects downstream claims processes.
This is what makes context-driven systems different. They generate outputs that are rooted in insurance logic, business constraints, and real operational workflows.
Generic AI offers general knowledge. Insurance-native AI delivers specialized intelligence.
Insurance is highly regulated. From CCPA, HIPAA and GDPR to regional compliance requirements, insurers operate under some of the most demanding compliance standards in any industry.
Generic AI wasn’t built with this level of scrutiny in mind. Insurance-native AI systems embed compliance from the ground up, ensuring outputs are traceable, governed, and auditable.
That means:
Whether you're handling sensitive submissions data or reconciling bordereaux, insurance-native AI ensures every action can be verified, traced, and trusted.
One of the well-documented limitations of general-purpose AI is hallucination: the generation of seemingly accurate but incorrect or fabricated information. According to a 2025 benchmark of 16 LLMs across 60 fact-based questions, OpenAI’s GPT-4.5 hallucinated 15% of the time—the lowest rate among all tested.
In insurance, hallucinations present real risks, from compliance violations to inaccurate claims decisions. Insurance-native AI addresses this head-on by prioritizing accuracy, transparency, and control.
These systems are:
In an industry where trust, accuracy, and compliance are non-negotiable, insurance-native AI provides a level of reliability that general-purpose tools simply can’t match.
Insurance IT ecosystems are complex, with legacy environments and a mix of vendor systems supporting critical operations. Most generic AI solutions require extensive customization to work within this infrastructure.
Insurance-native AI is built differently. It understands common industry formats and workflows out of the box, reducing implementation time and minimizing technical debt.
Brisc’s Submissions Agent, for example, doesn’t require a core system overhaul or complex customizations. You can simply email a submission — Excel, PDF, or SOV — and receive a clean, structured output in 60 seconds. An API is available to push results directly into your existing systems, whether that’s Guidewire, Marmalade, or something proprietary.
The result is fast deployment that doesn’t require you to rip-and-replace.
With growing pressure to improve efficiency and reduce costs, time-to-value is critical when adopting AI.
Insurance-native AI delivers on that promise because it’s already tuned for the work insurers actually do. It comes ready to tackle high-friction processes like submissions intake, claims triage, and bordereaux reconciliation, without months of custom training or system reconfiguration.
By accelerating implementation and minimizing integration overhead, insurers see results in weeks, not months. For example, Brisc’s Bordereaux Reconciliation Agent has helped clients reduce manual processing effort and achieve 59% cost savings compared to traditional reconciliation workflows.
To move beyond pilots, carriers, MGAs, and reinsurers need AI that works in their world, with their rules, data, and complexity.
Generic AI solutions weren’t built for this. Insurance-native platforms are.
They’re faster to deploy, more accurate, and designed to deliver ROI in environments where trust and compliance can’t be compromised.
If you’re serious about operationalizing AI, start with a platform that understands your business.
Book a demo to see what Brisc’s insurance-native platform can do.