Artificial intelligence (AI) is transforming the insurance industry. Tasks once handled by insurance experts — such as resolving claims, engaging customers and assessing risks — can now be automated entirely by AI agents for insurance.
These smart, automated systems quietly get on with important insurance work with unmatched speed, accuracy and efficiency. Unsurprisingly, adoption is on the rise. In 2024, 75% of health insurance providers leveraged AI for customer service, with 50% using it to enhance claims management.
In this article, we’ll explore how AI agents in the insurance industry are changing the game. We’ll look at:
- Different types of AI agents
- How insurance providers are leveraging them
- The steps involved in building an LLM-based AI agent
- How Intellias can help you navigate the complexities of AI
Understanding AI agents and their types
At their core, AI agents are intelligent software systems that can “perceive” their environment, make informed decisions and take actions to achieve specific goals. They do this through a combination of technologies, including advanced algorithms, machine learning (ML), natural language understanding (NLU) and generative AI.
Unlike popular AI tools such as ChatGPT, which serve as digital assistants, AI agents can work independently to solve problems or complete tasks. Where ChatGPT is reactive, AI agents are proactive. They can handle a variety of functions, including:
- Automating repetitive tasks
- Analyzing patterns and trends
- Enhancing customer interactions
- Processing large datasets
- Making informed decisions
Unlike traditional process-driven automation tools that simply implement a set of predefined instructions, AI agents can think for themselves about how best to achieve an outcome. They can also learn based on new experiences and data, enabling them to improve their performance and adapt to new challenges.
There are different types of AI agents, each designed to tackle specific use cases. Here are some key examples:
- Reactive agents. Also known as reflex agents, these AI models act based on what’s happening right now. They don’t store any memory, nor do they anticipate future outcomes or issues. Instead, they follow simple rules using an “if-then” logic. This makes them effective at simple, repetitive tasks such as eligibility checks or policy updates.
- Proactive agents. These take performance up a notch by anticipating future needs. They do this using predictive analytics, looking at patterns and trends in data to make informed decisions about what might happen next. This makes them ideal for tasks such as fraud detection or customer retention.
- Goal-based agents. These agents have a goal or set of goals that they must achieve. They then make decisions about how best to get to where they need to be. This might involve requesting information from other systems, taking actions or weighing up different pathways and choosing the most effective one. Examples include autonomous cars or AI chess players.
- Hybrid AI agents. These combine the qualities of reactive, proactive and goal-based agents, making them highly versatile. For example, an AI-powered hybrid agent could handle a customer’s live chat, suggest a tailored policy based on their profile and evaluate their long-term risk profile.
What are AI agents in insurance?
AI agents in insurance are specialized tools designed to tackle the unique challenges that insurance providers face. They play a crucial role in optimizing operations, improving accuracy and compliance, and enhancing customer service. For example, AI agents for the insurance industry can be used to:
- Automate customer interactions via AI chatbots
- Enhance contact center processes via virtual agents
- Identify fraudulent claims early, before they cause financial harm
- Ensure compliance with regulations and internal policies
- Improve the accuracy of policy underwriting and risk management
Because AI agents can handle multiple tasks simultaneously around the clock, they allow insurance providers to scale their operations while saving on costs.
According to McKinsey, there are four layers to AI capabilities for insurers. The more advanced insurers become in their AI adoption, the more personalized services become — and the more value they drive.
Source: McKinsey
Key components of AI agents for insurance
Now, let’s get down to the nitty-gritty. What can an insurance AI agent actually do? Below, we’ll look at some real-life applications that are transforming the way your insurance businesses operate.
1. Claims processing and management
Claim processing can be a huge drain on time and resources. From the customer side, delaying claim resolution can lead to frustration. AI solutions for insurance can automate claims assessment, cutting down processing times significantly and getting answers to customers fast.
AI agents in the insurance industry can also automate fraud detection. By identifying suspicious claims in real time, they help insurance providers investigate potential fraud before it impacts the bottom line. With insurance fraud costing more than $300 billion annually in the US alone, AI agents can make a tangible difference to the bottom line.
2. Underwriting and risk assessment
Risk profiling traditionally relied on human expertise and static data, often leading to issues down the line. AI agents in the insurance sector can analyze customer data from multiple dynamic sources and come up with highly reliable risk evaluations — all in a fraction of the time it once took.
Using predictive analytics, AI agents can anticipate potential risks before they happen. This enables insurance providers to improve the effectiveness of their underwriting processes, ultimately leading to a more profitable business. Big Four professional services firm Deloitte believes that the market for AI insurance underwriting could increase from around $40 million in 2024 to over $4.7 billion by 2032.
Source: Deloitte
3. Customer service and engagement
Responding to customer queries with limited human resources was once a major challenge for insurance providers. Now, with conversational AI solutions, businesses can ensure that customers receive instant responses around the clock.
Taking things further, advanced AI agents can tailor responses to the individual, based on customer data. They can anticipate customer needs and provide personalized recommendations. The result is happier customers and lower overheads.
4. Marketing and sales
AI is having a major impact on marketing and sales. Today, AI can enhance virtually all steps of a marketing campaign — from ideation to content creation, delivery and analytics.
By analyzing customer data, AI agents can deliver personalized offers and identify opportunities for upselling and cross-selling. They can also help insurance providers acquire new customers by identifying high-potential leads — and delivering targeted messaging that improves conversion rates.
5. Policy management
AI agents can take over the repetitive manual processes involved in policy management. For example, they can automatically generate and send policies to new customers, speeding up the approval process. They can also send automated reminders when policies are up for renewal. They can even update policy templates in line with changing internal or regulatory requirements.
6. Regulatory compliance
Compliance is a major issue for insurance providers, in what is a tightly regulated industry. AI agents in the insurance sector can help by automating compliance monitoring, ensuring adherence to rules and flagging issues as they arise. AI agents can also generate compliance reports in an instant and make detailed compliance recommendations based on current policies and processes.
7. Risk mitigation and prevention
In addition to assessing customer risk levels, AI agents in the insurance industry can help anticipate and prevent potential issues that could lead to insurance claims — such as equipment failure or damage. This is known as predictive maintenance, and it helps insurance providers save money, protect customers and keep operations running smoothly.
Beyond predictive maintenance, AI agents can help insurance providers monitor financial and operational risks. Based on real-time data insights, insurers can adjust premiums and make other necessary changes to mitigate future risks.
8. Product development and innovation
Insurance is a crowded market. To stand out, providers need to offer something fresh. AI agents can help here, using user data and market trends to think of improvements to existing offerings — or innovative new products. They can also help tailor products to individual needs.
Moreover, AI agents can help insurers maximize profitability while retaining customers through dynamic pricing models. Using predictive analytics and ML algorithms, they can adjust rates in response to changing data or market conditions.
9. Data management and analysis
Effective data management is central to a well-run insurance business. AI agents can help providers maximize the value of the data at their disposal, ensuring that it is organized, appropriately formatted and clean. They can spot duplicated data, flag inconsistencies and fill in gaps. AI agents can then turn this data into actionable insights that drive smarter decision-making.
10. Customer retention and loyalty programs
Customer retention is crucial to business success, with just a 5% increase in retention rates leading to a 25-95% increase in profits. AI agents can help you move the needle by identifying at-risk customers early. Your business can then target those customers with tailored messaging designed to keep them on board.
AI can also help improve client retention rates through personalized loyalty programs. For example, rewarding a car insurance customer for excellent long-term safety with a premium discount or free roadside assistance coverage.
11. Claims triage and prioritization
Not all claims are equal. A flooded home, for example, requires more urgent consideration than a minor roof leak. AI Agents can quickly sort through claims and rank them by severity and urgency. They can ensure that those in desperate need of resolution are pushed to the top of the pile.
Moreover, AI agents can look at staff workloads and automatically assign the right claims to the right people. By optimizing staff workloads, claims get resolved faster, resulting in happier customers.
12. Customer onboarding and verification
Customers now expect a slick, frictionless onboarding process. No wonder 82% of enterprise organizations see their onboarding strategy as a key driver of value. AI agents can help improve onboarding for insurance providers through self-service automation.
Your AI agent can quickly verify new customer identities through secure checks. They can simplify the sign-up process, guiding the customer through a series of simple steps and answering any questions they may have along the way. Once the sign-up process is complete, they can generate and send insurance policies, updates and other communications.
13. Document management and analysis
Insurers handle a lot of sensitive documents, making effective document management crucial. AI agents can take things beyond the level of legacy document management platforms by automating document processing. That means ensuring that files are named correctly, stored in the right location, given the correct access rights, etc.
AI in insurance can also provide automated text analysis. This uses NLU to read through digitized docs, pull out critical details and spot missing information.
14. Telematics and usage-based insurance (UBI)
Insurance telematics involves the long-distance transmission of real-time vehicle data to a central database. It allows insurance companies to take the guesswork out of car insurance, instead of basing premiums on actual car usage. This is known as usage-based insurance (UBI).
AI agents can take telematics data and use advanced algorithms to decide a fair premium that represents a driver’s actual risk level. Instead of placing drivers into broad risk buckets based on age, insurance providers can provide deals that reflect how often, at what times, and how cautiously or dangerously someone drives.
15. Market analysis and competitive intelligence
In an increasingly competitive market, gaining a deep understanding of what competitors are up to can be a game-changer for insurers. AI agents for insurance can analyse broader market trends and provide detailed competitive intelligence. This enables insurers to outsmart rivals and pinpoint gaps in the market.
AI agents can also help segment customers effectively, helping insurers target the right groups with the right policies.
16. Insurance policy recommendations
Finding the right policy can be overwhelming. With smart AI agents, customers can get personalized recommendations that match their specific needs. But that’s not all — AI can spot opportunities to upsell or cross-sell, with new policies and add-ons that both help the customer and increase revenue.
17. Omnichannel customer engagement
AI agents enable insurance providers to engage customers across multiple channels — e.g. via website chatbots, social media, email and even phone. They can track communication history, enabling them to understand the context of a customer’s query. And with access to all customer details, they can provide highly personalized communications that delight customers at every touchpoint.
Key benefits of AI agents in insurance
AI agents in insurance aren’t just another overhyped tech trend. They deliver real, tangible results for insurance providers and customers alike. Here’s why they’re a game-changer:
- Faster claim resolution. AI agents speed up claim resolution by automating assessments and prioritizing urgent cases, cutting wait times from days to hours.
- Cost savings. By automating repetitive tasks, AI agents reduce operational costs, freeing up budgets for innovation or competitive pricing.
- Improved accuracy. AI agents reduce the risk of human error, ensuring greater accuracy, consistency and compliance at scale.
- Enhanced customer experience. AI chatbots and virtual agents offer 24/7 self-service automation, ensuring that customers get instant answers to questions and personalized policy suggestions around the clock.
- Fraud prevention. Insurance AI agents use pattern recognition to identify fraudulent claims before payouts are made, potentially saving the insurance industry billions.
- Scalability. AI agents don’t need breaks and can handle thousands of tasks simultaneously, all while ensuring unbeatable levels of consistency and accuracy.
- Personalized services. From dynamic pricing to customized insurance products, AI agents use customer data to offer highly personalized offers that boost satisfaction and loyalty.
- Proactive risk management. AI can predict issues before they arise and alert policy-holders well in advance, lowering claim frequency and strengthening the bottom line.
The table below shows how and where AI investments are paying off for insurance providers. As you can see, a superior customer experience is by far the biggest benefit of AI at present.
Source: IMD
Building LLM-based AI agents for insurance
LLM-based AI agents are particularly useful for insurance providers. Their ability to interpret complex customer queries and insurance data — and respond with highly personalized, human-like communications — makes them the perfect tool for efficient customer service and engagement. Below, we’ll outline some key steps involved in building an LLM-based AI agent.
Planning and preparation
Before you can start building, you first need to lay the groundwork with detailed research and careful planning.
- Define the scope and objectives. What are you hoping to achieve by launching an AI agent? What areas do you want it to automate or improve? How will you measure success?
- Select an appropriate LLM. Choose a model that aligns with your needs and goals, with a proven track record with data management, analysis and customer interaction.
- Collect and prepare your data. Your AI agent will need access to the right data, such as customer details, claims records and previous communications. Make sure your data is as complete, extensive and accurate as possible.
- Choose a platform. There are plenty of platforms for building and deploying AI-powered agents, so make sure you choose one that aligns with your preferences and budget. Alternatively, you can build a custom stack from scratch.
Development and design
With the planning stage complete, it’s time to put your AI agent into development. This stage is highly complex, requiring deep expertise and knowledge of AI systems, data and technical infrastructure.
- Train the LLM. Use the data you collected and prepared in the planning stage to train your AI model, focusing on industry-relevant use cases and language.
- Develop the AI agent architecture. If your AI model is a brain, your architecture is its body, enabling it to connect it to the outside world through APIs, chatbot interfaces or telematics.
- Implement NLU. This stage ensures that your AI agent really understands what a customer is saying, whether they’re using industry lingo or more conversational language.
- Create knowledge integration systems. Integrate your AI agent with external data sources — like weather forecasts or fraud databases — to enrich its insights.
- Develop reasoning and analytical capabilities. Beyond NLU, your AI agent needs to be able to weigh up risks, flag suspicious behavior and achieve its goals without compromising on security or compliance.
- Design output generation and summarization. Ensure that your AI agent represents your business in its outputs. That means nailing the tone of voice, avoiding technical jargon and providing customers with clear information that anyone can understand.
Ensuring ethics and usability
Great tech needs a moral compass and a human touch. This stage ensures that your AI agent is both ethical and intuitive to interact with.
- Implement ethical and bias-mitigation measures. Make sure your AI is always fair in its assessments. Train it to avoid bias or negative profiling based on demographics or physical characteristics.
- Create a slick user interface. Interacting with your AI agent should be simple and intuitive. Focus on an outstanding user experience to ensure that your business and customers derive maximum value from your AI.
Testing, deployment and beyond
Now your AI agent is ready for action. Here are some steps to ensure successful implementation in the real world.
- Testing and validation. Before launch, stress-test your AI agent. Does it catch fraud or answer queries effectively? Can it handle high loads without fail? If not, it’s back to the drawing board.
- Deployment and scaling. With all issues ironed out, you can launch your AI agent into the wild, ensuring it can scale to meet growing use cases and demand.
- Continuous improvement and updating. Even after deployment, your AI agent is a work in progress. Continuously test and update your AI in line with new data, feedback and technological innovations.
- Documentation and training. Remember — your AI agent is just a tool. If your staff don’t know how to use it effectively, you’ll fail to maximize its potential.
How can Intellias help you build AI agents for insurance?
Success with AI agents isn’t a given. To realize the huge benefits they offer, you need access to deep technical expertise and industry knowledge. This is exactly what we offer at Intellias. We help insurance providers worldwide transform legacy systems into modern, agile platforms. AI adoption — and AI agents in particular — is at the heart of this.
Our expert team can guide you through every step involved in designing, building and deploying AI agents, including
- Providing strategic consultation to align AI with your business goals
- Developing custom AI agents that meet your specific needs
- Ensuring seamless integration with your existing systems
- Providing continuous support to help you optimize your AI agents
- Helping you adopt innovative AI solutions that push the boundaries of what’s possible
