Reverse Engineering Large Language Models to Create Insurance Raters

Reverse engineering large language models (LLMs) to create an AI that can deliver home and auto insurance quotes isn't just a fascinating technical exercise; it's a direct path to addressing a real-world need. As more people shop online for personalized services, like insurance, they expect quick, accurate quotes and clear guidance. LLMs offer a foundation for building such AI, but they're trained on general data rather than specifics like actuarial tables or regional insurance policies. The challenge, then, is to adapt an LLM's general abilities for the specifics of insurance quoting.

Understanding Reverse Engineering for LLMs

Reverse engineering, in this context, means analyzing and deconstructing existing LLMs to understand how they interpret and generate responses. Why reverse engineer? Because each LLM, like OpenAI's GPT models, Google's Bard, or Meta's LLaMA, is trained with its own methods and data sets, which make them excel at different tasks. By reverse engineering several models, we can see which techniques and patterns work best in crafting a bot for insurance quotes.

For instance, GPT-4 is highly effective at conversational engagement and nuance. It could smoothly answer a user's questions about what factors affect their premium, but it won't necessarily produce an accurate quote. Other models, maybe those specialized in calculations or data processing, might be better at retrieving and parsing data. By dissecting several LLMs, we can combine the best elements—some models' data-handling skills and others' conversational abilities—to build a robust insurance quoting AI.

Building AI for Home and Auto Insurance Quotes

A successful AI for home and auto insurance quotes would need several specific capabilities:

Understanding User Input with Precision

Insurance quotes are personal. The AI needs to ask targeted questions to understand a user's needs and circumstances—things like driving history, type of vehicle, or property location. LLMs are great at interpreting language, but tailoring this for insurance means programming it to know when and why to ask certain questions. Learn more about our approach to personalized AI solutions.

Here's where the model needs to identify critical keywords or phrases and then follow up with context-relevant questions. For example, if someone mentions "new driver," the AI would know to ask about age, car type, and prior driving record, as these factors heavily influence quote estimates.

Handling Calculations and Data Processing

Insurance quotes rely on hard numbers derived from historical data, risk calculations, and statistical models. Most LLMs, even the best ones, aren't inherently designed for this level of mathematical processing. However, combining an LLM with a dedicated backend that pulls data from actuarial databases or connects withindustry-standard APIs for real-time pricing could help bridge this gap. Our ACORD Me Not solution demonstrates how we approach data processing challenges.

Let's say the AI needs to generate an auto insurance quote for a 2020 Honda Civic. The backend could pull up base rates for that vehicle, apply factors based on the user's location, driving history, and other personal details, and present a quote. In this setup, the LLM manages the conversation flow, while the backend handles the calculations.

Ensuring Regulatory Compliance and Accuracy

Insurance is heavily regulated. Each state has unique rules governing rates, and these policies change. Reverse engineering an LLM for this purpose requires building in knowledge of regulatory compliance. So, a model that's excellent at analyzing legal language (perhaps fine-tuned withlegal and insurance documents) would be useful here. Check out our POE platform for examples of how we handle document processing.

To keep quotes accurate, the AI must stay updated on regulations and integrate with reliable data sources. This could involve regular fine-tuning and, potentially, hybrid models that call upon both LLM capabilities and dedicated rule-based systems that manage state-specific requirements. Our Edison AIplatform already incorporates some of these capabilities for insurance intelligence.

Bringing It All Together: A Hybrid Approach

To achieve a reliable insurance-quoting AI, the reverse engineering of various LLMs could lead us to a hybrid model—one that leverages both conversational fluency and mathematical precision. Here's how it might work:

Interaction Layer (LLM-Based)

The interaction layer is user-facing, running on an LLM that excels at natural conversation. This layer can gather details, answer questions, and guide the user. It needs to feel intuitive so that users stick with it through the often tedious data entry that quoting requires. This is similar to how our Bonnieplatform handles client interactions, but specialized for insurance quoting. See our features pagefor more examples of conversational AI.

Calculation and Compliance Layer (Backend and Specialized Models)

Behind the scenes, a calculation layer does the number-crunching based on actuarial formulas, and a compliance layer ensures everything aligns with state and federal laws. When a user completes the questionnaire, the LLM hands off the gathered information to this backend, which produces the final quote.

Continuous Learning and Updating

Finally, any insurance-quoting AI needs constant updating. Fine-tuning it on new data—especiallylegal changesand emerging risk factors (like the rise of electric vehicles)—is crucial. Reverse engineering LLMs could also reveal patterns and gaps, helping developers identify areas to improve the AI's responsiveness or accuracy over time. Learn about our continuous learning approach with Edison AI.

For more insights on how AI is transforming the insurance industry, check out our article on Insurance Industry Trends to Watch in 2025.

The Future of AI in Insurance

An AI-driven quoting system is more than just a quoting tool; it can also educate users about the factors affecting their premiums. For example, after giving a quote, it could suggest ways to reduce costs, like taking a defensive driving course or bundling policies. This advice, tailored to the user's profile, would add real value beyond just price estimates. See how we're implementing this in ourSmallGiant case study.

Using reverse-engineered LLMs to build an insurance-quoting AI isn't just feasible—it's a logical next step in making insurance more accessible and understandable. And as AI advances, it could eventually integrate with IoT devices (like a smart home's safety sensors or a car's telematics) to offer even more precise and personalized rates.

At Strawberry Antler, we're already exploring these possibilities with ourCustom AI Solutions, which can be tailored to specific insurance needs. If you're interested in learning more about how AI can transform your insurance operations,contact us for a consultation.

Conclusion

Reverse engineering LLMs for insurance rating is a complex but rewarding endeavor that combines the best of conversational AI with domain-specific knowledge and regulatory compliance. As these technologies continue to evolve, we can expect to see more sophisticated, accurate, and user-friendly insurance quoting systems that make the process of getting insured simpler and more transparent for everyone. Learn more about ourpricing models for AI implementation or contact our team to discuss your specific insurance AI needs.