Insurance Ground Truth Database for Multi-Source Conflict Resolution
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Solution Overview
Problem
Current insurance systems are cumbersome, inefficient, and lack the ability to resolve conflicts in data sources, provide up-to-date recommendations, and offer natural language interactions for insurance agents and underwriters.
Innovation Solution
Utilizing artificial intelligence (AI) and machine learning (ML) to recommend insurance coverage changes based on customer profiles, build a ground truth insurance database, aid underwriting, and assist insurance agents by providing accurate, up-to-date recommendations in natural language through chatbots and voicebots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If current electronic systems aggregate insurance data from different sources, then data quantity increases, but data reliability deteriorates due to conflicts between sources
Solution Approach 1:
The patent introduces an intermediary component (conflict resolution module) that mediates between multiple data sources. This module receives data from various sources, identifies conflicts, and resolves them using predefined rules or machine learning algorithms to determine the most reliable data, thereby maintaining data reliability while aggregating from multiple sources.
Solution Approach 2:
The system implements feedback mechanisms where the resolution outcomes from conflicts are fed back into the system to improve future conflict resolution. Machine learning models are trained on resolved conflicts to enhance their ability to identify and resolve data conflicts, thereby improving data reliability over time while maintaining multi-source aggregation.
2Ease of operation
If current systems require customers to contact agents manually for policy change recommendations, then service personalization improves, but productivity deteriorates
Solution Approach 1:
The patent implements self-service capabilities where the system automatically monitors customer data, identifies when policy changes may be beneficial, and generates recommendations without requiring customer initiation. The system proactively reaches out to customers with personalized recommendations based on their data changes, maintaining personalization while dramatically improving productivity.
Solution Approach 2:
The system performs preliminary actions by continuously monitoring customer data and pre-identifying potential policy change opportunities before customers request them. This allows the system to have recommendations ready when customers are actually interested, improving both personalization and efficiency by avoiding manual triggering while maintaining timely, relevant recommendations.
3Device complexity
If current systems do not use up-to-date data, then data processing simplicity improves, but recommendation accuracy deteriorates
Solution Approach 1:
The patent implements dynamic data processing where the system continuously updates customer data in real-time as new information becomes available. The recommendation engine dynamically adjusts its analysis based on the most current data, ensuring recommendation accuracy improves as data freshness improves, while the modular architecture prevents excessive complexity by processing only relevant changes.
4Productivity
If current systems provide recommendations in non-natural language form, then processing efficiency improves, but ease of operation for agents deteriorates
Solution Approach 1:
The patent replaces traditional mechanical data processing and recommendation delivery systems with natural language processing and generation capabilities. The system processes data efficiently using automated algorithms but delivers recommendations in natural language formats that are easily understood and acted upon by insurance agents, combining processing efficiency with agent usability.
Data Source
AI summary
The following relates generally to building a ground truth insurance database. In some embodiments, one or more processors: (1) receive potential insurance database information comprising (i) insurance company application (app) information, (ii) anonymized insurance claim information, (iii) police report information, and/or (iv) auxiliary information; (2) retrieve, from an insurance ground truth insurance database, existing ground truth insurance information by querying the ground truth database based upon the potential insurance database information; (3) determine if the potential insurance database information should be added to the insurance ground truth database by comparing the potential insurance database information to the existing ground truth insurance information; and/or (4) if the potential insurance database information should be added to the insurance ground truth database, add the potential insurance database information to the insurance ground truth database.