Machine-Learning Loss Prediction for Faster Claim Assessment
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Solution Overview
Problem
Existing SaaS providers face inefficiencies in claim processing, particularly in insurance claims, due to time-consuming manual procedures and suboptimal communication with policy holders, leading to frustration and delays.
Innovation Solution
A computing system implementing machine-learning and AI technologies to optimize claim processes, including guided content capture, dynamic scripting, and adaptive communication strategies to streamline information gathering and automate negotiations, utilizing large language models for efficient summarization and resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual procedures are used for claim processing, then accuracy can be maintained through human review, but processing time increases significantly
Solution Approach 1:
The patent introduces machine-learning models as intermediary systems between claim data and human reviewers. These models automatically analyze claim information, perform initial assessments, and generate predictions, serving as a mediator that handles routine analysis tasks while allowing human experts to focus on complex cases requiring judgment, thereby reducing overall processing time while maintaining accuracy
Solution Approach 2:
The system performs preliminary analysis and classification of claims using automated machine-learning models before human review. By pre-processing claims, generating initial assessments, and organizing data in advance, the system reduces the workload for human reviewers and accelerates the overall processing workflow without sacrificing accuracy
2Ease of operation
If traditional communication methods are used with policy holders, then simplicity is maintained, but engagement and satisfaction decrease
Solution Approach 1:
The patent implements dynamic communication strategies that adapt to individual policy holder needs, preferences, and behaviors. The system automatically adjusts communication channels, timing, and content based on real-time data analysis, transforming static communication into a flexible, responsive process that maintains simplicity while significantly improving engagement and satisfaction
3Productivity
If extensive computing resources are allocated to claim processing, then processing capability increases, but resource efficiency decreases
Solution Approach 1:
The patent employs machine-learning models that optimize resource allocation by dynamically adjusting processing parameters based on claim complexity, priority, and historical patterns. The system identifies and focuses computational resources on high-value or complex claims while using automated rules for routine cases, changing the parameters of resource distribution to achieve higher productivity with reduced overall computing resource consumption
Data Source
AI summary
A computing system can receive incident data corresponding to an incident involving a property of a user. The system may then generate a total loss prediction indicating a damage repair amount for the property based on the incident data.