Vehicle Damage AI Triage for Bodily Injury Claim Coding
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Solution Overview
Problem
The rising number of fraudulent or exaggerated bodily injury claims in vehicle accidents, particularly low-impact claims, is causing increased costs and inefficiencies for insurance carriers, claims managers, and analysts, leading to higher premiums and longer claim processing times.
Innovation Solution
An adaptive analytics system utilizing machine learning models, including computer vision and classifiers, to analyze vehicle damage and correlate it with potential bodily injuries, providing predicted medical diagnostic codes and confidence indicators to improve claim evaluation accuracy and consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual claim evaluation methods are used, then analysts can review each claim in detail, but the process becomes time-consuming and inefficient leading to longer claim processing times
Solution Approach 1:
The system performs preliminary analysis by automatically evaluating vehicle damage images and generating predicted injury codes before the analyst reviews the claim. This pre-processing step filters out obvious cases and prepares analysis results in advance, reducing the time analysts need to spend on each claim while maintaining evaluation accuracy.
Solution Approach 2:
The patent introduces an AI-based intermediate system that acts as a mediator between the claim data and the analyst. The system processes vehicle damage images, generates predicted injury codes, and presents structured analysis results to analysts, who then make final determinations. This intermediary layer automates routine evaluation tasks while preserving human judgment for complex cases.
2Productivity
If more analysts are hired to handle increased claim volume, then claim processing capacity increases, but operational costs increase leading to higher premiums
Solution Approach 1:
The system enables self-service evaluation by allowing the AI model to independently assess claims and generate predictions without requiring extensive human intervention. The automated analysis of vehicle damage images and generation of injury codes allows the system to handle increased claim volume without proportionally increasing analyst headcount, thereby controlling operational costs.
Solution Approach 2:
The patent transforms the claim evaluation process from a purely manual human-driven process to an automated system-driven process. By changing the operational parameters from human labor-intensive to machine-learning-based automated analysis, the system increases processing capacity while reducing the marginal cost of handling additional claims.
3Reliability
If analysts manually evaluate each claim without assistance, then they can maintain consistent decision-making, but the complexity and volume of claims lead to increased litigation risk
Solution Approach 1:
The system provides structured feedback to analysts through predicted injury codes, confidence scores, and analysis results generated from vehicle damage images. This feedback mechanism ensures that all analysts evaluate claims using the same objective criteria derived from the AI model, improving decision-making consistency and reducing variability that could lead to litigation.
Solution Approach 2:
The patent segments the claim evaluation process into distinct components: automated image analysis, predicted injury code generation, confidence scoring, and final analyst determination. This segmentation allows each component to be optimized independently and ensures that the automated portion provides consistent, objective analysis that reduces litigation risk while maintaining overall decision reliability.
Data Source
AI summary
A computer-implemented method comprises providing images and attributes of a damaged vehicle that has been damaged in a collision event to a trained computer vision machine learning model, wherein responsive to the first inference input, which in response provides indicators of physical damage sustained by the damaged vehicle during the collision event; providing the indicators to a trained classifier machine learning model, which in response provides predicted standard medical diagnostic codes related to bodily injury sustained by an occupant of the damaged vehicle during the collision event and confidence indicators representing levels of confidence that the one or more predicted standard medical diagnostic codes are correct; and providing the predicted standard medical diagnostic codes and the confidence indicators to an analyst for use in evaluating a bodily injury claim related to the occupant of the damaged vehicle and the collision event.