Presenter: Luk Anapoli, Industry Solutions Consultant
Topic: Implementation of Retrieval-Augmented Generation (RAG) for insurance claim processing
RAG Overview
Definition: RAG is the process of enhancing large language models (LLMs) with contextual information, incorporating proprietary data for better answers.
Purpose: Accelerate and improve claim adjustment operations in insurance companies.
Workflow and Architecture
User Prompt: Undergoes vectorization and embedding.
Vector Retrieval: Using MongoDB Atlas Vector Search to get similar vectors.
Document Retrieval: Fetching the most relevant documents from the collection using these vectors.
Combination with LLM: Feeding documents along with the original question to the LLM, resulting in an enriched and informative response.
Example: Car Accidents Due to Adverse Weather
Adjuster's Task: Assess damage and determine compensation.
User Prompt: Focus on claims related to adverse weather (e.g., heavy rain).
Embedding and Retrieval: Vector search retrieves documents containing conceptually similar information (not necessarily an exact match).
Enhanced Response: Adjuster gets a summary of weather-related accidents (including rain, hail, and fire) from the LLM.
Business Application and UI Demonstration
Image Search Interface: Users can drag and drop images to find similar photos and associated claims quickly.
Chat Box: Allows users to type questions or select predefined ones. Example: Asking about average loss amount for weather-related claims.
Results Display: Includes relevant documents and claim descriptions, aiding quicker damage estimation.
Advantages of RAG
Speed and Accuracy: Faster and more accurate damage assessment.
Enhanced Customer Experience: Improved service quality.
Intuitive Interaction: Natural language processing reduces the need for special system skills.
Leveraging Unstructured Data: Utilizing PDFs, photos, and videos previously buried in data lakes.
Impact on Insurance
Beneficiaries: Claim handlers, underwriters, and customer service operators.
Data Strategy: A robust data model can serve multiple personas effectively.
Conclusion
Summary: RAG in conjunction with MongoDB and LLMs improves claim processing efficiency and customer experience.
Call to Action: Reach out to the Industry Solutions team for more information.