Collision Damage Analysis for Consistent Bodily Injury Prediction
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Solution Overview
Problem
The rise in fraudulent and exaggerated bodily injury claims following vehicle accidents, coupled with increasing claim severity and complexity, leads to inefficiencies and higher costs for carriers and analysts, including inconsistent workflows, high turnover, and difficulty in negotiating with attorneys.
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 likelihoods and confidence indicators to assist analysts in evaluating claims.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual analysis methods are used for bodily injury claims, then analysts can evaluate each claim individually, but the process becomes time-consuming and inconsistent across different analysts
Solution Approach 1:
The system performs preliminary analysis of vehicle damage images and attributes before the analyst reviews the claim. The machine learning models pre-calculate injury likelihood scores and confidence indicators, so the analyst receives pre-processed information that requires minimal additional evaluation time while maintaining high accuracy
Solution Approach 2:
The system creates a digital representation of the physical vehicle damage through computer vision models that generate standardized damage indicators and attributes. This digital copy can be consistently analyzed by the machine learning model without requiring the original physical evidence to be re-examined, enabling rapid and consistent evaluation across multiple claims
2Reliability
If more detailed analysis of vehicle damage is performed to improve injury prediction accuracy, then the reliability of injury likelihood prediction improves, but the complexity of the system increases
Solution Approach 1:
The system segments the complex analysis task into distinct components: a computer vision model that extracts damage indicators from images, and a separate machine learning classifier that predicts injury likelihood from those indicators. This segmentation allows each component to be optimized independently and simplifies the overall system architecture while maintaining high prediction reliability
Solution Approach 2:
The system introduces an intermediary layer of damage indicators and attributes that bridge the gap between raw vehicle damage images and final injury predictions. This intermediary representation standardizes the information flow and enables the machine learning model to process complex damage patterns without requiring direct complex image analysis, thus reducing system complexity while improving 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, 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 a predicted likelihood that bodily injury was sustained by an occupant of the damaged vehicle during the collision event and a confidence indicator representing a level of confidence that the predicted likelihood that bodily injury was sustained is correct; and providing the predicted likelihood of bodily injury and the confidence indicator to an analyst for use in evaluating a bodily injury claim related to the occupant of the damaged vehicle and the collision event.