InsurTech: 5 benefits of technologies in Insurance Sector

Published on Nov 13, 2017

Updated on Jun 9, 2022

InsurTech is a buzzword nowadays where a variety of technologies are set to transform the traditional insurance industry. In the last two years, insurers have already transformed themselves digitally to offer convenience, security, choice, and a seamless experience to their customers.

Accenture estimates that insurance companies can increase their annual profitability by 20% with the right investment in the technology.

Internet of Things (IoT), telematics, drones, the blockchain, smart contracts, and artificial intelligence (AI) are providing new ways to measure, control, engage customers, reduce cost, improve efficiency and increase customer experience.

Here are five ways Insurers can stay ahead in the market and successfully fulfill high customer expectations.

1. Lower Insurance rates:

– Fitness apps or wearable devices:

Staying fit has many perks. Some of the fitness apps like Wysa and wearable devices help maintain weight, and food habits and boost energy and mood. And most importantly they can help save a huge amount of expenses related to health insurance costs. Numerous insurance providers have tapped into wearable devices to keep motivating their customers to stay fit and healthy and offer them discounts and benefits based on fitness levels.

– Self Driving car:

Self Driving cars can help in reducing the chances of accidents and lower life insurance rates. Since road deaths are a significant percentage of deaths in the entire world, any slight downward change will ultimately lead to lower deaths and hence life insurance claims.

2. Fraud Prevention:

Insurance fraud costs companies billions of dollars per year across the globe. Insurance companies should establish a technology framework, tap into advanced automation and analytics, and take steps to prevent it.

– Digital Signature:

Digital signature technology is without a doubt lowering fake insurance account activation and hence a fraud. For example, a digital signature can prevent fraud- insurance purchased after the accident can be brought down with digital signatures verifying the actual date.

– Data analytics:

The technology involves data mining tools and quantitive analysis. Data analytics can be applied to detect fraud. Predictive analytics is useful to improve the fraud detection process, helping prevent claims payouts. Analytics on claims and fraud transactions helps enhance risk management.

3. Lower underwriting cost:

–IoT

According to IoT Analytics, the global number of connected IoT devices is likely to grow at 9%, with 12.3 billion active endpoints. By 2025, there will likely be more than 27 billion IoT connections, which will have a significant impact on the availability of real-time information that insurers can use for better pricing/underwriting. Drones are satellites on steroids at least as far as underwriting is concerned. Satellites have dramatically changed how home insurance policies are written due to fire. Everything can be captured via drone footage even the houses that get covered behind the trees. Captured data can be used for underwriting purposes.

4. Billing efficiency:

Billing systems are not only integrated but now can accept varied forms of payments allowing ultimate flexibility to the customer and thereby making the billing systems efficient. The automated systems inform and remind customers of approaching due dates for premiums thereby lowering unintentional defaults.

Digital wallet has become one of the most widely used platforms for payment systems. Insurance companies are leveraging payment gateways like Google Play to sell insurance to users. Last year, SBI General Health Insurance launched Arogya Sanjeevani on Google Pay Spot to offer standard coverage at affordable premiums and improve the penetration of health insurance in the country.

5. Specialized insurance:

Each type of insurance is different from the other and the factors that are suited to one are not suited to the other. This requires the insurance agents to have specialized knowledge and the internet helps. however, Machine learning is vitally important here. It has the capability to learn and analyze billions of patterns and identify suitable underwriting clauses as well as identify specific customized plans for the customers based on the data provided. This can change the customer perception of the insurance company and provide an engaged customer who is likely to stay longer.

Dinghy, is a pay-by-the-second insurance provider that customizes coverage for freelancers and businesses where customers may switch their policies on and off as needed without any upfront premiums, interest, credit checks, or fees.

6. Smart and Faster Claim Processing and Settlement:

–AI-Powered Chatbots:

Claim settlement has been one of the pressing issues in insurance. With intense competition looming in the market, delay in the claim settlement gives a bad experience to the customer who prefers to switch to another brand. Insurance providers worldwide have been investing in AI-powered insurance chatbots to enhance customer experience. Metromile can validate 70% to 80% of claims instantly using AVA, an app based-claims assistant.

7. Data-driven pricing

–Telematics:

Innovation has become one of the top priorities for insurers today due to rapid change in customer demand. The usage-based insurance market is projected to hit over $190 billion by 2026, telematics is allowing carriers to capture user data and create personalized usage-based insurance products.

For example, auto insurance was based on a pay-as-you-drive model where customers use to pay a premium based on the distance covered. But with technological innovation, insurers are working on a pay-how-you-drive model where customers can get discounts based on their driving skills.

Rise in demand for innovative solutions, intelligent experiences, and speedier processes has led to technological disruption in the insurance industry. According to IDC, IT spending in the insurance industry will increase globally at a CAGR of 6.0% by 2024, touching $135 billion. With continuous investment in technology, insurers are working on improving customer experience and operational efficiency to maximize profitability in the long run.

Thanks you Scott W Johnson, owner at WholeVsTermLifeInsurance.com for providing your valuable information on how technologies are helping Insurance industry.

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