Voice-AI Event Warnings for Faster Insurance Claim Intake
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing insurance claim processes are inefficient and time-consuming, often causing frustration for policy holders and delays for providers due to manual procedures and lack of personalized user experiences.
Innovation Solution
A computing system utilizing large language models and artificial intelligence to automate and individualize insurance claim processes through adaptive flow engines, voice-AI technology, and machine-learning techniques for efficient information gathering, settlement negotiation, and service assignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual claim processing procedures are used, then claim handlers can personally assess each claim, but processing time increases and efficiency decreases
Solution Approach 1:
The patent introduces an AI-based automated claim assessment system as an intermediary between the claimant and human adjusters. This automated system pre-evaluates claims using machine learning models trained on historical data, providing recommendations to human adjusters. This intermediary layer maintains assessment quality through AI accuracy while dramatically reducing processing time for routine claims, resolving the contradiction between reliability and productivity.
Solution Approach 2:
The patent segments the claim processing workflow into different tiers: fully automated assessment for straightforward claims using AI models, and human-adjuster review for complex or high-value claims. This segmentation allows routine claims to be processed rapidly by AI while reserving human expertise for cases requiring judgment, thereby improving overall productivity without sacrificing assessment quality on complex claims.
2Productivity
If standardized claim processes are used, then processing efficiency improves, but personalized user experiences are reduced
Solution Approach 1:
The patent implements dynamic claim processing pathways that adapt to each claim's characteristics. The system automatically adjusts the level of automation and personalization based on claim complexity, type, and value. Simple claims follow streamlined automated processes for efficiency, while complex claims receive more personalized human attention. This dynamic adaptation resolves the contradiction by providing efficiency where appropriate and personalization where needed.
Solution Approach 2:
The patent applies different processing qualities to different segments of the claim portfolio. High-volume, low-complexity claims receive standardized automated processing with efficient turnaround, while low-volume, high-complexity claims receive personalized human review. This local differentiation of processing quality allows the organization to maximize efficiency for the majority of claims while maintaining personalization for cases where it matters most.
3Measurement precision
If more claim information is collected, then claim assessment accuracy improves, but information gathering time increases
Solution Approach 1:
The patent implements preliminary automated data collection and validation before claims reach human adjusters. The system automatically gathers relevant information from multiple sources, validates data quality, and prepares comprehensive claim packages in advance. This preliminary action ensures that when human adjusters review claims, all necessary information is already assembled and verified, improving assessment accuracy without requiring extended information gathering during the review process.
Solution Approach 2:
The patent replaces manual information gathering and validation processes with automated digital systems. Machine learning models automatically extract, verify, and cross-reference claim data from various sources, substituting the mechanical process of manual information collection with automated computational processes. This substitution maintains high assessment accuracy while dramatically reducing the time required to gather and verify claim information.
Data Source
AI summary
Embodiments include a computing system, computer-implemented method and non-transitory computer readable medium for predicting events and voice-AI warnings. According to embodiments, data corresponding to a predicted event is received, and users that are predicted to be affected by the predicted event are identified. A voice-AI engine is initiated to perform a voice-AI call to the identified users, where the voice-AI call provides a warning to each of the users.