AI-Powered Knowledge Management for Insurance Proposals
Project Details
Duration
06 Months
Team Size
8 specialists
Industry
Financial Services
Technologies
OpenAI
PineCone VectorDB
React
Huggingface
Langchain
Python
Project Overview
In today’s fast-paced insurance industry, agencies must provide clients with accurate and competitive insurance proposals quickly. Our client, a US-based independent insurance agency, specializes in offering customized insurance solutions across auto, home, health, and other categories. Unlike captive agencies tied to a single insurer, this brokerage firm enables clients to compare quotes from multiple providers, ensuring they receive the best coverage at the best rates.
However, to craft tailored proposals, agents must sift through numerous policy documents from different insurance companies—each with its own structure, terminology, and coverage details. This process is highly manual, time-consuming, and prone to errors, impacting both productivity and customer experience.
Challenges & Solutions
Challenges
-
Document Overload: With multiple insurance providers, agents must navigate through a vast amount of policy documents, endorsements, and terms to extract relevant details for each client.
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Inefficient Search: Traditional keyword-based search methods often return irrelevant results, making it difficult for agents to find the exact information they need.
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Time-Consuming Process: Preparing a comprehensive proposal requires manually reading, comparing, and compiling data from multiple sources, leading to delays in client responses.
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Access Control & Compliance: Ensuring that only authorized agents can access specific documents while maintaining compliance with data protection policies is crucial.
Solutions
Solution: AI-Powered RAG-Based Search
To assist the agent to query the documents quickly during the proposal preparation process, we developed a Gen AI RAG (Retrieval-Augmented Generation) solution that allows agents to search across multiple policy documents using natural language queries.
Key Features:
-
Semantic Search & Natural Language Understanding
Agents can ask questions in plain English (e.g., "What are the liability coverage limits for auto policies from Provider X?"), and the system retrieves the most relevant information from stored documents. Unlike traditional keyword searches, our AI-powered retrieval method understands context, synonyms, and industry-specific terminology.
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Knowledge Base Creation
The platform allows the agency to create multiple knowledge bases, each containing a collection of policy documents from different insurance providers. Knowledge bases can be categorized based on insurance type (Auto, Home, Health, etc.), provider, or specific regulations, making retrieval faster and more organized.
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Role-Based Access Control (RBAC)
A built-in RBAC module ensures that agents only access documents relevant to their role or assigned clients, maintaining data security and compliance. Administrators can define access levels, restricting sensitive policy details to authorized personnel.
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Enhanced Productivity & Accuracy
The solution eliminates the need to browse multiple PDFs or portals, allowing agents to focus on client interactions rather than document management.
Results & Impact
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Improved Accuracy: Reduces human errors and ensures that clients receive the most up-to-date policy details.
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Better Client Experience: Agents can respond to customer inquiries promptly, improving trust and satisfaction.
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Scalability: As the agency expands its partnerships, new documents can be easily added to the knowledge base without disrupting operations.
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