Why Insurance-Native AI Wins Over General Solutions
By Sanjay Malhotra, CEO, Brisc AI —
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.
Key Takeaways
- Insurance-native AI is purpose-built to handle the complexity, regulation, and workflows unique to insurance.
- Generic AI lacks context and often hallucinates, posing significant risk in compliance-driven environments.
- Vertical, agentic AI platforms deliver faster ROI, cleaner data, and more trustworthy outputs by acting within insurance context.
What Is Insurance-Native AI?
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.
Why Insurance-Native AI?
1. Built on Insurance Expertise
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.
2. Designed for Compliance, Auditability, and Control
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:
- Human-in-the-loop (HITL) oversight for transparency and escalation
- Audit trails and document retention to support internal reviews and regulatory reporting
- Role-based access controls and data security aligned with PHI, PII, and financial regulations
- Explainability by design, helping teams and regulators understand how outputs were generated
Whether you’re handling sensitive submissions data or reconciling bordereaux, insurance-native AI ensures every action can be verified, traced, and trusted.
3. Delivers Accurate Outputs Grounded in Real Data
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:
- Tuned with verified insurance context, documents, workflows, and constraints
- Validated against industry- and business-specific rules and policy structures
- Bounded by safeguards that prevent the generation of outputs that would violate regulations or introduce operational risk
- Designed with human-in-the-loop (HITL) oversight, escalating ambiguous or conflicting scenarios for expert review
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.
4. Plug-and-Play Deployment
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.
5. Delivers Measurable ROI Faster
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.
The Future of Insurance AI Is Vertical, Agentic, and Built-In
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.
To see what Brisc’s insurance-native platform can do, book a demo today.
About Brisc AI
Brisc is an AI platform purpose-built for insurance. We help insurers, MGAs, and reinsurers automate high-effort processes like submissions, claims, and bordereaux reconciliation—so teams can work faster, with fewer errors, and unlock the full value of their data. Our AI Agents are trained on insurance-specific context, enabling accurate automation from day one, while Brisc Insights lets users access and act on real-time intelligence across their workflows. For more information, please visit https://brisc.ai.
Source: Brisc AI
Tags: adjusters, Artificial Intelligence (AI), Brisc AI, InsurTech, InsurTech Spotlight