AI Claim Negotiator for Faster Insurance Claim Processing
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Solution Overview
Problem
Existing Software as a Service (SaaS) providers face inefficiencies in claim processing, particularly in insurance claims, due to time-consuming manual procedures and suboptimal use of computational resources, leading to frustration for policy holders and inefficiencies for providers.
Innovation Solution
A computing system that utilizes artificial intelligence and machine learning to optimize claim processes, including guided content capture, dynamic scripting, and real-time communications, to streamline information gathering and automate negotiation, reducing processing time and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual procedures are used for claim processing, then human judgment and flexibility are maintained, but processing time increases and resource usage increases
Solution Approach 1:
The system enables automated claim processing where AI models independently evaluate claims, determine approvals or denials, and communicate decisions without requiring human intervention for each individual claim. This self-service approach allows the system to handle high volumes of claims simultaneously, dramatically improving processing speed while reducing time loss associated with manual procedures.
2Measurement precision
If comprehensive information gathering is performed manually, then accuracy is improved, but computational resource requirements increase
Solution Approach 1:
The system performs preliminary information gathering and validation automatically before claims are submitted for human review. By pre-collecting and organizing relevant data, verifying completeness, and preparing claims in advance, the system ensures accurate information is available when needed while reducing the computational burden during the actual claims processing phase.
Solution Approach 2:
The patent replaces manual mechanical information gathering processes with automated digital systems that use machine learning models to efficiently collect, validate, and analyze claim data. This substitution maintains high accuracy through sophisticated algorithms while significantly reducing computational resource consumption compared to manual processing of the same volume of claims.
3Productivity
If automated systems are implemented, then processing efficiency is improved, but system complexity increases
Solution Approach 1:
The system employs universal AI models and standardized processing workflows that can handle multiple types of claims across different insurance products through a single integrated platform. This multi-functionality approach improves processing efficiency by eliminating the need for separate systems for different claim types while managing complexity through reusable components and consistent architectures.
4Ease of operation
If real-time communication is implemented, then user engagement is improved, but computational resource requirements increase
Solution Approach 1:
The system implements periodic communication with policy holders at key milestones in the claims process rather than continuous real-time updates. This approach maintains user engagement by providing timely information about claim status, approval decisions, and next steps while significantly reducing computational resource requirements compared to sustained real-time communication systems.
Data Source
AI summary
A computing system can obtain an information corpus corresponding to a claim event involving a user. The system may then execute an artificial intelligence negotiator using the information corpus to perform automated negotiation process with the user.