Autonomous Vehicle Insurance: Present and Near Future

Published on Sep 25, 2019

Updated on Nov 6, 2019

We’re about to witness the evolution of autonomous vehicles from Level 0 to Level 2. While Level 0 is completely human-driven; Level 1 vehicles can control braking and parallel parking themselves. Level 2 vehicles can operate automatically, but with a human ready to control exceptional situations.

The success of self-driving cars depends solely on the safety it brings to transportation. With increased safety, will we even need insurance for autonomous vehicles?

Perhaps, the traditional insurance policies might face a setback. But, autonomous vehicles will certainly open new avenues for innovative insurance products.

The Stevens Institute of Technology predicts that there would be over 23 million fully autonomous vehicles by 2035 in the US alone.

To stay competitive with the changing dynamics of auto insurance, insurers need to address new risks. But before, let’s take a look at potential risks in the autonomous vehicle insurance sector.

Autonomous vehicle insurance: the evolution of autonomous cars from Level 0 to Level 5

Potential Impact of ‘Autonomous Vehicles’ Revolution

The shift to autonomous vehicles tends to bring dramatic changes in auto insurance premiums.

Instead of individual policies, researchers foresee insurance policies turning towards original equipment manufacturers (OEMs) and service providers such as ride-sharing companies. The new auto insurance products would be an outcome of the following transportation changes.

New Road Regulations

With autonomous vehicles on the roads, safety regulations are prone to change. For instance, the US National Highway Traffic Safety Administration intends to reconsider its current safety standards to accommodate AVs in existing transportation. But, this reformation will take the presence of human drivers into account.

Increased Safety and Reduced Claims

With increased safety and reduced accident claims, the revenues from traditional premium policies might decline.

Insurers often follow a “no-fault” system to lower auto insurance costs by taking small claims out of the courts. For minor injuries, insurers compensate their policyholders regardless of who was at fault in the accident.

However, fender-benders would be more than it is with autonomous vehicles. Also, blockchain in insurance would become integral to investigate the root cause of the accident. And, of course, there won’t be much scope for lenient “no-fault” policies.

Change in Insurance Liability

Traditional liability insurance pays for the policyholder’s legal responsibility to others for bodily injury or property damage. With autonomous vehicles, the liability is going to shift towards OEMs, suppliers, or car-rental service providers.

Underwriting?

Currently, automakers must adhere to around 75 safety standards. This underwriting considers that a licensed driver will control the vehicle. The safety standards are going to change with more AVs on roads.

The present-day premium is high for a handful of autonomous vehicles because of insufficient data with underwriters and actuaries. However, chances are, major OEMs will cover the insurance premiums in the vehicle cost.

For instance, Tesla, one of the pioneers of autonomous vehicles, provides auto insurance at 30% lower rates than other insurance providers. Tesla having a better understanding of its vehicles’ technology and repair costs, believes can provide low-cost insurance. This is also a threat to insurance carrier fees.

Scope for New Autonomous Vehicle Insurance Products

Accenture estimates that autonomous vehicles will generate at least $81 billion in new insurance revenues in the US between 2020 and 2025. It also foresees opportunities for insurers in cybersecurity, product, and infrastructure landscapes. Let’s take a look at new auto insurance avenues.

Cyber Security

While AVs ensure safety, there are unidentified cybersecurity threats. Vehicles fueled by IoT technology deal with comprehensive telematics data. Capturing every moment of the user proposes risks like identity theft, privacy invasion, misuse of personal information, and attacks from ransomware. According to the Center for Strategic and International Studies and McAfee, globally cybercrimes cost around $600 billion annually. The shared data from autonomous vehicles bring the financial sector at risk.

On the other hand, monitoring the performance of vehicles and the driver’s behavior behind the wheel can reduce claim investigation turn around time.

Therefore, future insurance products will also focus on moral and financial threats to passengers.

Product Liability

The product liability insurance might shift from automotive to sensors and algorithms behind the autonomous vehicle. The OEMs will be also liable for communication or Internet connection failure along with machinery and software failures.

Insurance Against Existing Infrastructure

It will take more than 30 years for autonomous vehicles to completely dominate transportation. The upcoming insurance products will take existing infrastructure into account. For example, AVs need insurance if it damages due to puddles or potholes on the road.

Also, car ownership tends to decline with rental and pay-as-you-use models. This opens a fleet-level opportunity for insurers for driverless cars.

Source: Accenture X Stevens Institute of Technology “Insuring Autonomous Vehicles” report

Insurers need to adapt to the rapid technological advancements. Cloud-based insurance workflow platforms or IaaS (Insurance as a Service) models help in achieving operational gains in the entire insurance value chain.

Concluding Remarks

AVs are going to dominate the world’s highway because of improved safety and convenience. Companies can leverage this opportunity to introduce innovative autonomous vehicle insurance products.

Growing IoT is blurring the fine-line between different verticals of insurance. To stay competitive, insurers should also indulge in creating new distribution channels and partnerships with OEMs and technology service providers.

Generic AI is no longer enough. Domain-specific AI is the new enterprise advantage.

From hospitals to factories to insurance carriers, organizations are learning the hard way: horizontal AI platforms might be impressive, but they’re often blind to the realities of your industry.

Here’s the new playbook: intelligence that’s narrow, not general. Context-rich, not context-blind.
Welcome to the age of domain-specific AI agents— from underwriting co-pilots in insurance to care journey managers in hospitals.

Why Generalist LLMs Miss the Mark in Enterprise Use

Large language models (LLMs) like GPT or Claude are trained on the internet. That means they’re fluent in Wikipedia, Reddit, and research papers; basically, they are a jack-of-all-trades. But in high-stakes industries, that’s not good enough because they don’t speak insurance policy logic, ICD-10 coding, or assembly line telemetry.

This can lead to:

Hallucinations in compliance-heavy contexts
Poor integration with existing workflows
Generic insights instead of actionable outcomes

Generalist LLMs may misunderstand specific needs and lead to inefficiencies or even compliance risks. A generic co-pilot might just summarize emails or generate content. Whereas, a domain-trained AI agent can triage claims, recommend treatments, or optimize machine uptime. That’s a different league altogether.

What Makes an AI Agent “Domain-Specific”?

A domain-specific AI agent doesn’t just speak your language, it thinks in your logic—whether it’s insurance, healthcare, or manufacturing.

Here’s how:

Context-awareness: It understands what “premium waiver rider”, “policy terms,” or “legal regulations” mean in your world—not just the internet’s.
Structured vocabularies: It’s trained on your industry’s specific terms—using taxonomies, ontologies, and glossaries that a generic model wouldn’t know.
Domain data models: Instead of just web data, it learns from your labeled, often proprietary datasets. It can reason over industry-specific schemas, codes (like ICD in healthcare), or even sensor data in manufacturing.
Reinforcement feedback: It improves over time using real feedback—fine-tuned with user corrections, and audit logs.

Think of it as moving from a generalist intern to a veteran team member—one who’s trained just for your business.

Industry Examples: Domain Intelligence in Action

Insurance

AI agents are now co-pilots in underwriting, claims triage, and customer servicing. They:

Analyze complex policy documents
Apply rider logic across state-specific compliance rules
Highlight any inconsistencies or missing declarations

Healthcare

Clinical agents can:

Interpret clinical notes, ICD/CPT codes, and patient-specific test results.
Generate draft discharge summaries
Assist in care journey mapping or prior authorization

Manufacturing

Domain-trained models:

Translate sensor data into predictive maintenance alerts
Spot defects in supply chain inputs
Optimize plant floor workflows using real-time operational data

How to Build Domain Intelligence (And Not Just Buy It)

Domain-specific agents aren’t just “plug and play.” Here’s what it takes to build them right:

Domain-focused training datasets: Clean, labeled, proprietary documents, case logs.

Taxonomies & ontologies: Codify your internal knowledge systems and define relationships between domain concepts (e.g., policy → coverage → rider).
Reinforcement loops: Capture feedback from users (engineers, doctors, underwriters) and reinforce learning to refine output.
Control & Clarity: Ensure outputs are auditable and safe for decision-making

Choosing the Right Architecture: Wrapper or Ground-Up?

Not every use case needs to reinvent the wheel. Here’s how to evaluate your stack:

LLM Wrappers (e.g., LangChain, semantic RAG): Fast to prototype, good for lightweight tasks
Fine-tuned LLMs: Needed when the generic model misses nuance or accuracy
Custom-built frameworks: When performance, safety, and integration are mission-critical

Use Case	Reasoning
Customer-facing chatbot	Often low-stakes, fast-to-deploy use cases. Pre-trained LLMs with a wrapper (e.g., RAG, LangChain) usually suffice. No need for deep fine-tuning or custom infra.
Claims co-pilot (Insurance)	Requires understanding domain-specific logic and terminology, so fine-tuning improves reliability. Wrappers can help with speed.
Treatment recommendation (Healthcare)	High risk, domain-heavy use case. Needs fine-tuned clinical models and explainable custom frameworks (e.g., for FDA compliance).
Predictive maintenance (Manufacturing)	Relies on structured telemetry data. Requires specialized data pipelines, model monitoring, and custom ML frameworks. Not text-heavy, so general LLMs don’t help much.

Strategic Roadmap: From Pilot to Platform

Enterprises typically start with a pilot project—usually an internal tool. But scaling requires more than a PoC.

Here’s a simplified maturity model that most enterprises follow:

Start Small (Pilot Agent): Use AI for a standalone, low-stakes use case—like summarizing documents or answering FAQs.

Make It Useful (Departmental Agent): Integrate the agent into real team workflows. Example: triaging insurance claims or reviewing clinical notes.
Scale It Up (Enterprise Platform): Connect AI to your key systems—like CRMs, EHRs, or ERPs—so it can automate across more processes.

Think Big (Federated Intelligence): Link agents across departments to share insights, reduce duplication, and make smarter decisions faster.

What to measure: Track how many tasks are completed with AI assistance versus manually. This shows real-world impact beyond just accuracy.

Closing Thoughts: Domain is the Differentiator

The next phase of AI isn’t about building smarter agents. It’s about building agents that know your world.

Whether you’re designing for underwriting or diagnostics, compliance or production—your agents need to understand your data, your language, and your context.

Ready to Build Your Domain-Native AI Agent?

Talk to our platform engineering team about building custom-trained, domain-specific AI agents.

Further Reading: AI Code Assistants: Revolution Unveiled

Autonomous Vehicle Insurance: The Present and Near Future