Predicted-Event Voice-AI Warning System for User Outreach
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Solution Overview
Problem
Existing insurance claim processes are inefficient and time-consuming, often causing frustration for policy holders and delays for policy 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
1Ease of operation
If manual procedures are used for claim processing, then personalized user experiences can be provided, but processing time increases and efficiency decreases
Solution Approach 1:
The system implements self-service through AI agents that autonomously perform claim assessment, negotiation, and settlement without requiring manual intervention from adjusters. The AI agents independently gather information, evaluate claims, negotiate with policyholders, and execute settlements, enabling the system to serve itself while maintaining personalized experiences and reducing processing time.
Solution Approach 2:
The patent replaces manual mechanical processes with automated AI-based systems. Human adjusters are replaced by AI agents that use machine learning models to assess claims, natural language processing to communicate with policyholders, and automated negotiation algorithms to reach settlements. This substitution eliminates manual procedures while preserving personalized service through adaptive AI interactions.
2Reliability
If manual claim processing procedures are used, then complex negotiation can be handled, but productivity decreases and delays occur
Solution Approach 1:
The claim processing system is segmented into distinct AI agents with specialized functions: information gathering agents, claim assessment agents, negotiation agents, and settlement execution agents. Each agent handles specific tasks autonomously, allowing parallel processing of multiple claims simultaneously while maintaining high-quality negotiation through specialized AI models trained on historical negotiation data.
Solution Approach 2:
The system dynamically adjusts negotiation parameters such as settlement offers, deadlines, and communication strategies based on real-time analysis of claim characteristics, policyholder behavior patterns, and historical outcomes. AI agents modify negotiation parameters adaptively to optimize both settlement quality and processing speed, handling complex negotiations efficiently through parameter optimization rather than manual intervention.
3Productivity
If automated systems are implemented, then processing speed increases, but personalized user experiences may be reduced
Solution Approach 1:
The AI agents employ dynamic communication strategies that adapt to individual policyholder preferences, communication styles, and claim situations. The system continuously learns from interactions, adjusting its approach to match user expectations while maintaining high processing speed through automated decision-making. Personalization is achieved through dynamic parameter adjustment rather than static manual customization.
Solution Approach 2:
The system implements continuous feedback loops where AI agents monitor policyholder responses, engagement levels, and satisfaction indicators in real-time. This feedback informs subsequent communication and negotiation strategies, enabling the automated system to personalize experiences adaptively. The feedback mechanism allows the system to learn and adjust to individual user preferences while maintaining high throughput through automated processing.
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.