Claims genAI Copilot

The team was tasked with enhancing the efficiency of Mutual of Omaha’s claims processing operations by introducing a generative AI-driven assistant. Historically, claims benefit specialists relied on legacy systems and manual review of dense policy and rider documentation—methods that are not only time-consuming but also difficult to scale. In collaboration with Mutual of Omaha’s internal GenAI team, the Design Studio team developed an AI-powered solution designed to provide fast, accurate access to policy information through natural language queries. The team implemented a Retrieval-Augmented Generation (RAG) architecture that leverages large language models via AWS Bedrock to retrieve and deliver form-specific content in real time. By integrating document-level metadata filtering, semantic embedding, and a user-friendly Gradio interface, the solution enables claims associates to interact with hundreds of complex insurance documents in a conversational and guided manner. This project sought to modernize the existing system with intelligent automation that reduces claim processing time, enhances training for new hires, and improves the overall user experience for employees engaging with critical policy content.

Publication Details

Journal
Insecta mundi
Published
2025-05-01
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
0.00
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article

Claims genAI Copilot

Insecta mundi
Geochemistry and Geologic Mapping
article

Claims genAI Copilot

article en

Abstract

The team was tasked with enhancing the efficiency of Mutual of Omaha’s claims processing operations by introducing a generative AI-driven assistant. Historically, claims benefit specialists relied on legacy systems and manual review of dense policy and rider documentation—methods that are not only time-consuming but also difficult to scale. In collaboration with Mutual of Omaha’s internal GenAI team, the Design Studio team developed an AI-powered solution designed to provide fast, accurate access to policy information through natural language queries. The team implemented a Retrieval-Augmented Generation (RAG) architecture that leverages large language models via AWS Bedrock to retrieve and deliver form-specific content in real time. By integrating document-level metadata filtering, semantic embedding, and a user-friendly Gradio interface, the solution enables claims associates to interact with hundreds of complex insurance documents in a conversational and guided manner. This project sought to modernize the existing system with intelligent automation that reduces claim processing time, enhances training for new hires, and improves the overall user experience for employees engaging with critical policy content.

Insecta mundi
Openalex Percentile: Top 64%
Geochemistry and Geologic Mapping
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