ML Claim Processing Platform With Confidence-Based Review Routing
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Solution Overview
Problem
Existing claim processing systems for commercial and shared economy services are inefficient, leading to excessive resource expenditure and suboptimal processing of claims.
Innovation Solution
A computing platform utilizing machine learning to process claims, including training models with historical data, automating claim processing, and providing insights through a digital straight-through experience, allowing for reduced operational costs and faster, consistent claim outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual claim processing methods are used, then processing accuracy and decision quality may be maintained through human review, but processing speed and operational efficiency deteriorate due to excessive resource expenditure and time-consuming manual intervention
Solution Approach 1:
The system enables automated self-service claim processing where the machine learning model independently evaluates claims, determines settlements, and executes payments without requiring manual human intervention for routine claims. The system serves itself by automatically routing claims based on confidence scores and handling straightforward cases autonomously.
Solution Approach 2:
The patent replaces the mechanical human review process with an automated machine learning system that uses algorithms to evaluate claims, assess damage, determine fault, and make settlement decisions. This substitution eliminates the need for manual mechanical processing while maintaining or improving decision quality through consistent application of trained models.
2Productivity
If automated machine learning processing is implemented, then processing efficiency and operational cost reduction are improved, but processing reliability and accuracy may worsen without sufficient manual review oversight
Solution Approach 1:
The system implements feedback loops where claims that fall below confidence thresholds are routed to human reviewers, whose decisions are fed back into the system to continuously train and improve the machine learning models. This creates a self-correcting mechanism that enhances reliability over time while maintaining high throughput for confident automated decisions.
Solution Approach 2:
The system dynamically adjusts the level of automation based on claim complexity and model confidence. Straightforward claims with high confidence scores are processed entirely automatically, while ambiguous or complex claims dynamically transition to human review. This dynamic approach optimizes both efficiency and reliability based on real-time conditions.
3Measurement precision
If comprehensive manual review is performed on all claims, then processing accuracy and decision quality are improved, but processing time and operational complexity worsen due to excessive manual intervention requirements
Solution Approach 1:
Instead of performing complete manual review on all claims, the system applies partial automated processing to high-confidence claims that require minimal or no human review. This partial action approach maintains accuracy for straightforward cases while eliminating unnecessary review time, reserving comprehensive human analysis only for complex or low-confidence claims.
Solution Approach 2:
The system applies different levels of processing quality to different claims based on their characteristics. High-confidence, straightforward claims receive streamlined automated processing with minimal human involvement, while complex or ambiguous claims receive more thorough review. This local differentiation of processing intensity optimizes both accuracy and time efficiency.
4Adaptability or versatility
If traditional claim processing systems are used, then system complexity may be reduced with simpler infrastructure, but adaptability and scalability worsen due to inability to handle varying claim volumes and types efficiently
Solution Approach 1:
The machine learning platform is designed as a universal system that can handle multiple types of claims (auto, property, liability, workers' compensation) and varying volumes through a single integrated architecture. The same core platform adapts to different claim categories by applying appropriate trained models and configurations, eliminating the need for separate specialized systems.
Solution Approach 2:
The system achieves adaptability through parameter changes in the machine learning models, adjusting thresholds, confidence levels, and processing rules based on claim type and volume. By modifying software parameters rather than hardware infrastructure, the system flexibly adapts to varying conditions without increasing physical complexity.
Data Source
AI summary
Aspects of the disclosure relate to using machine learning methods to produce commercial and shared economy insights. A computing platform may receive historical claim processing information. The computing platform may train a machine learning model using the historical claim processing information, which may configure the machine learning model to output new claim processing information based on claim information. The computer platform may receive a new claim, including claim information, and may process the new claim using the machine learning model, which may result in the new claim processing information. The computing platform may send, to a user computing device, the new claim processing information and one or more commands directing the user computing device to display the new claim processing information, which may cause the user computing device to display the new claim processing information.