The myth that AI can never replace human is what kept us without worrying, but findings suggest otherwise. AI despite designed to have never feelings are found to be more receptive to emotions than humans to its customers. Wall Street journal reported that Allstate, a leading carrier in the Personal Lines found that their AI driven interactions provided more compassionate interactions with their customers, proving to be more empathetic than their own representatives. Allstate models to write claims related emails aligning with the company specific terms while also reducing the industry jargons for the customers to understand.
This high degree of adaptability makes us thinks how far AI can work in complete autonomy without us, humans directly intervening. The impact that it can have on the customer journey is something that insurers need to think about as they need to scale up when the market demands. In this era where people want personalized value and seamless experiences especially in insurance, potential of such technologies like Agentic AI can’t be ignored.
Chatbots vs Agentic AI
While chatbots are designed to provide a scripted and predefined response or reaction to an already set forth workflow, Agentic AI on the other hand operates on cognitive intelligence with full autonomy. Unlike traditional chatbots, which carry out tasks based on a structured decision tree, Agentic AI can comprehend, learn and act on it independently. The multi-model inputs- which includes text, voice real time data and Retrieval-augmented generation (RAG) is used in the reasoning to make decisions based on the situations they are put in. This cognitive intelligence is guided by advanced cognitive layers such as Semantic Memory (factual insurance knowledge), Episodic Memory (past customer interactions), and Procedural Memory (learned underwriting or claims processes).
For instance, A chatbot in claims processing can assist a customer by providing a FAQ that is standardised or guide them through a normal claims submission form that is basic. But Agentic AI on the other hand can do beyond that by understanding the gravity of any damage in an accident to analyse the picture provided using RAG(retrieval augmented generation), cross reference it with any similar claims data and incident that has happened before and offer a proactive solution through the submission to settlement journey. More than dumping information to a customer it focuses on streamlining the resolution process relayed to claims or its adjustments.
Allstate redefined customer interactions with the help of AI, but how does Agentic AI work in the insurance value chain especially in underwriting and claims settlement to enable faster, more precise decisions, minimize waiting time and fair policy pricing which are key factors that drive customer trust and satisfaction.
Smart Underwriting
Insurers uses multi-agent system driven by AI to perform risk assessment for underwriting. This can ensure more accurate, smart and efficient results compared to the traditional methods. There are specialised agents for each function, and they play a crucial role in the decision-making process and cutdown long process.
Here’s how Agentic AI are transforming underwriting:
Information Gathering Agent – Collects data from multiple sources, including customer records, claims history, and market insights, ensuring a more precise risk profile. (Utilizes Semantic and Episodic Memory to recall factual knowledge and past interactions.)
Risk Analysis Agent – Uses AI-driven insights to evaluate risks, allowing insurers to make underwriting decisions. (Relies on Procedural Memory to apply learned underwriting frameworks.)
Fraud Detection Agent – Unusual pattern detection to flag potential fraud to reduce losses and improving claims integrity. (Uses Episodic Memory to recognize anomalies based on past fraud cases.)
Pricing Strategy Agent – Risk Assessment and Market trend evaluation for competitive pricing. (Leverages Semantic Memory for factual market insights and Procedural Memory for pricing methodologies.)
Approval Agent – Makes final underwriting decisions or suggests further review. (Depends on Procedural Memory to apply learned underwriting guidelines.)
Regulatory Agent – Ensures that underwriting and pricing decisions comply with evolving legal and industry standards. (Uses Semantic Memory to stay updated on regulations and compliance rules.)
End-to-End Claims Processing
Many insurers use Agentic AI system to extract data from the forms submitted during the claims processing. This can help in verifying the data with existing databases and report if there are any potential fraud signals just like how we talked about in underwriting.
Data capture from claim form documents including text inputs, checkbox selections, and attachments. (Utilizes Semantic Memory to recognize and interpret structured and unstructured data.)
Automated Data Storage – Extracted details, such as claim ID, policy number, and claim type, are stored in a centralized database for easy retrieval. Relies on Semantic Memory to categorize and store factual information
Workflow Automation – Agents can automate approvals and faster settlement of claims in case of any adjustments are required, ensuring smooth workflow. Uses Procedural Memory to apply learned claims processing rules and streamline decision-making.
Such AI-driven capabilities can be relevant across multiple insurance lines, including life, auto, cyber, property, health, and travel insurance.
With the evolution of underwriting and claims automating complex tasks and improving precision, insurance solutions become more personalised to meet the needs of different customers.
Conclusion
The rise of AI Agents marks an important shift in how insurers interact with customers, process claims, and assess risk. Insurers must rethink about their approach in delivering customer service, as they deliver more seamless and personalised experience. So, it’s not just a tool but a “strategic imperative” to stay relevant and retain their customers.
