AI Claim Negotiator for Faster Insurance Settlement Processing
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Solution Overview
Problem
Existing SaaS providers face inefficiencies in computational resource usage and claim processing times for insurance claims, leading to frustration for policy holders and delays 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 negotiations, reducing the time required for claim processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual claim processing methods are used, then claim processing accuracy and thoroughness are maintained, but claim processing time is excessive and computational resource usage is inefficient
Solution Approach 1:
The patent replaces manual mechanical claim processing with an automated AI system that uses machine learning models, natural language processing, and computer vision to perform claim intake, validation, and triage. This substitution dramatically reduces processing time while maintaining or improving accuracy through consistent application of validation rules and automated document analysis.
Solution Approach 2:
The AI negotiator system enables self-service claim processing by automatically interacting with policyholders, gathering necessary information, validating claims against policy terms, and determining appropriate triage categories without requiring human adjuster intervention for routine claims. This automates the service delivery while reducing operational costs.
2Reliability
If comprehensive claim information gathering is performed manually, then claim accuracy is maintained, but resource consumption and processing complexity increase
Solution Approach 1:
The AI system performs multiple functions within a single integrated platform: claim intake through natural language conversation, document validation using machine learning models, information extraction via optical character recognition, claim triage using predictive analytics, and automated negotiation. This multi-functional approach reduces the need for separate manual processes while maintaining comprehensive claim accuracy.
Solution Approach 2:
The system incorporates continuous feedback loops where the AI negotiator learns from interactions with policyholders, validates information against policy databases, and adjusts its triage recommendations based on historical claims data and outcomes. This feedback mechanism ensures accurate claim processing while the system becomes increasingly efficient over time.
Data Source
AI summary
Embodiments include a computing system, computing device, computer-implemented method and non-transitory computer readable medium for executing an artificial intelligence-driver negotiator. Embodiments obtain an information corpus corresponding to a claim event involving a user, and upon obtaining the information corpus, embodiments execute an artificial intelligence negotiator using the information corpus to perform automated negotiation process with the user.