AI in Insurance: Takeaways from AI for Data-driven Insurers Webinar

5 minutes, 54 seconds read

Published on Apr 15, 2020

The adoption of AI has increased exponentially across the business ecosystem in the past couple of years. Yet, Insurance still lags behind many industries due to the nature of its business. However, the ease of convenience that has come with AI implementations has made it indispensable to Insurers. So, where has the demand for the convenience come from? ‘Modern Insurance Customer’. The millennials today demand 24×7 service at their fingertips. They are keener towards information provided on digital channels and more likely to use social media and texting for Insurance interactions. To suffice the needs and demands of the modern insurance customer, AI integration is needed.

Role of AI in Insurance

Currently, AI is playing a pivotal role in transforming Insurance processes such as Claims, Underwriting, Customer Service, Marketing, fraud detection etc. For example, AI chatbots are being used to handle customer service which has led to a significant reduction in cost and optimization of human resources. According to a report by Deloitte on Unraveling the Indian Consumer, India has the world’s largest millennial population of 440 million in the age group of 18-35 years. Internet users in the country are expected to increase from 432 million in 2016 to 647 million by 2021, taking internet penetration from 30 per cent in 2016 to 59 per cent in 2021.

AI-based technologies will be needed to meet the evolving demands of modern insurance customers.

According to the State of AI in Insurance 2020 report, nearly half of all Insurance executives surveyed believe that Automated processing can add value to their customer experience journeys. Nationwide is using artificial intelligence to help analyse customer interactions so it can solve customers’ problems earlier. Using AI and NLP, the insurer identified opportunities for reducing inefficiencies. And the result was more than half of all email enquiries could be resolved by guiding users towards digital channels instead.

During the webinar, we polled the audience to gauge their motivation for implementing AI in their business processes. 44% felt that Claims Processing was the main reason to adopt AI into their business Insurance processes.

The quick poll was in line with Mantra Labs’ State of AI in Insurance report 2020 which found that 74% of the respondents leaning towards the adoption of AI in Claims Processing.

The webinar addressed some of the key challenges faced by Insurers, reasons behind these challenges and how we can approach these challenges to bridge the disconnect.

Data in Silos

Most businesses that have data kept in silos face challenges in collaboration, execution and measurement of their bigger picture goals. Accumulating information in silos may not give accurate insights into improving engagement, which leads to impersonalized content that doesn’t speak to the customer. However, models well-trained on historic data, don’t necessarily perform better with live data. The challenge is that data is often needed before it is even possible to conduct a proof of concept — and sourcing the right data can be both time consuming and costly. The right approach to this issue would be to treat Data as the centrepiece for transformation. Insurers should engage with data scientists/consultants to review the quality of your data. Data exploration exercises need to be performed to challenge/validate the existing assumptions about data captured and stored within the org.

People, Expertise and Technical Competency

Many organizations face a challenge in finding the right ‘Skill and Talent’ for developing AI strategies and implementing them. Critical skill-sets like data scientists, cloud specialists, machine learning engineers, and AI engineers are essential to keep pace. Several Industry experts have also relayed that many AI-based projects and proof-of-concept work do not take off the ground due to lack of quality data at the disposal of such skilled professionals — derailing their availability/ usefulness for hiring purposes. Securing the right data science teams and training the right amount of data needed to support algorithm development can improve confidence levels for organizations.

Clear Vision, Process & Support from Executive Leadership

Often the reason for the failure of AI projects is due to lack of clear thought process from the top management. According to a recent BCG report, there is a big gap between expectations and planning. Most companies want to create a long-term competitive advantage with AI and expect to see a major impact from AI within 5 years. The big disconnect, however, is that only 39% of enterprises had an AI strategy to go with it. Insurers shouldn’t run headfirst into moonshot AI projects. Instead, they should take a more measured approach that identifies a simple problem or problems (use case) that AI can address. Insurers must ensure that the goals of AI projects must be in line with organization goals.

Technology and Vendor Selection

Many Insurers today fail to understand how AI can be leveraged for their business. There is a lot of unseen effort that goes behind any AI implementation project. They are not sure which AI-based technologies to be used for solving a particular problem. According to the State of AI in Insurance 2020 report, InsurTech funding in 2019 reached $6B revealing a stronger emphasis by insurance organizations to fast-track the progress and development made by startups in tackling age-old insurer ills with AI-fueled innovations. InsurTechs are seen as advantageous because they can add value by scaling their operating models at incredible speed owing to their nimble size.

There are tools, products developed harnessing AI-based technologies which have helped optimize several core insurance businesses. The Haven Life Risk Solutions team, in partnership with MassMutual, has developed a platform that uses both a rule engine and machine learning models to analyze the application and third party data in real-time. It can now help MassMutual make many underwriting decisions without human underwriter intervention, and in some cases also without a medical exam. Motor Insurance Claims is where AI is currently driving maximum efficiency. There are certain gaps that are being faced by insurers which can be resolved with AI platforms specific towards claims processing. FlowMagic, a visual AI platform developed by Mantra Labs focuses on streamlining Insurer workflows.

Concluding Remarks

In these challenging times, AI is already helping Insurance companies find their competitive edge, and stay operationally agile even during pandemics. Queries which are being addressed by chatbots help humans to handle more complex issues. It cannot be stressed enough that the next couple of months would be difficult for several businesses including Insurance.

Companies across the world have already started making plans to ensure business continuity in this pandemic. AI or automation will play a crucial role in streamlining various processes and accelerate innovation to adapt to the dynamic environment and ensure long term stability.

Our host Parag Sharma interacted one on one with participants, during an interactive Q&A session where insights were shared with the audience. The discussions centred around some thought-provoking questions such as tracking AI performance once implemented, the role of AI in helping to reach Bharat, the potential for AI in telemedicine, etc.

Articles from Parag:

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

4 Key Takeaways from AI for Data-driven Insurers Webinar