ML Loss Prediction for Faster Vehicle Claim Triage
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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 use of computational resources, leading to frustration for policy holders and delays for providers.
Innovation Solution
A computing system that optimizes claim processes using machine learning and large language models (LLMs) for efficient information gathering, real-time communication, and automated negotiation, integrating with user and call center representative devices to streamline claim handling and reduce processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual procedures are used for claim processing, then accuracy can be maintained through human judgment, but processing time increases and productivity decreases
Solution Approach 1:
The patent replaces manual mechanical review processes with machine learning models that automatically analyze incident data, communicate with users, and negotiate settlements. The ML system substitutes human reviewers while maintaining accuracy through algorithmic analysis of claim patterns and outcomes.
Solution Approach 2:
The system enables self-service through automated ML-driven communication with users and automatic negotiation processes. The machine learning model independently handles claim assessment, user interaction, and settlement negotiation without requiring continuous human intervention, thereby increasing productivity while maintaining quality through automated decision-making.
2Loss of information
If comprehensive information gathering is performed manually, then complete claim data can be collected, but computational resources are wasted and processing efficiency decreases
Solution Approach 1:
The machine learning model performs partial information gathering by selectively requesting only the specific data needed for accurate claim assessment. Rather than manually collecting all possible information, the system intelligently determines the minimum necessary dataset, reducing computational overhead while ensuring completeness of essential claim information.
Solution Approach 2:
The system dynamically adjusts information gathering parameters based on the specific claim type and incident characteristics. The ML model modifies its data collection strategy in real-time, requesting different sets of information for different scenarios, thereby optimizing computational resource usage while maintaining information completeness for accurate claim processing.
3Productivity
If automated systems are implemented for claim processing, then productivity increases and processing time decreases, but system complexity increases
Solution Approach 1:
The machine learning model serves multiple functions within a single system architecture: it analyzes incident data, communicates with users, negotiates settlements, and makes decisions. This multi-functionality consolidates what would otherwise require separate systems for each task, increasing productivity while managing complexity through a unified ML-driven platform.
4Productivity
If machine learning models are used for automated negotiation, then processing time decreases and productivity increases, but measurement precision of settlement outcomes may be compromised
Solution Approach 1:
The machine learning model incorporates feedback mechanisms that learn from negotiation outcomes and user responses. The system continuously refines its negotiation strategy based on actual user behavior patterns and settlement results, maintaining accuracy while operating at high speed. The feedback loop ensures that automated negotiation precision improves over time through experience.
Data Source
AI summary
Embodiments include a computing system, computing device, computer-implemented method and non-transitory computer-readable medium for generating loss predictions. In embodiments, incident data is received, corresponding to an incident involving a vehicle of a user, and based on the incident data, a total loss prediction is generated for the vehicle, where the total loss prediction is indicative of whether the vehicle is repairable or totaled.