Pre-FNOL Damage Assessment System Using Image Recognition
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Solution Overview
Problem
Insurance policy holders face challenges in quantifying damage compensation and assessing the impact on their insurance policy after an accident, with limited options for determining repair costs and potential policy changes before submitting a claim.
Innovation Solution
A pre-FNOL (First Notification of Loss) system that assesses damage by prompting policyholders to provide accident details and photos, determining repair costs, and facilitating insurance policy changes, allowing for direct payment or repair shop arrangements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional claims processing procedures are used, then insurance compensation can be determined, but policyholders have limited options for quantifying damage and assessing policy impact before filing a claim
Solution Approach 1:
The system performs loss assessment actions before the formal claims process (pre-FNOL). It collects damage information, photographs, and vehicle data in advance, processes this information through image recognition and data analysis, and provides policyholders with estimated repair costs and policy impact information before they file an official claim, enabling informed decision-making about whether to proceed with a claim
2Measurement precision
If detailed damage assessment is performed, then accurate repair cost information is provided, but the complexity of the assessment process increases
Solution Approach 1:
The system enables policyholders to perform self-assessment by capturing photographs of damage themselves using their mobile devices and submitting vehicle information. The automated image recognition algorithms process these user-submitted photos to identify and quantify damage, reducing the need for complex manual inspection processes while maintaining assessment accuracy
Solution Approach 2:
The system replaces traditional mechanical/physical inspection methods with automated image recognition and data processing. Computer vision algorithms analyze photographs to detect damage, estimate repair costs, and generate assessments, substituting human inspector labor and complex physical measurement processes with automated computational methods
3Productivity
If pre-FNOL assessment is implemented, then claim processing efficiency is improved, but the system requires integration with multiple external systems
Solution Approach 1:
The system acts as an intermediary layer between policyholders and the formal claims processing system. It collects and preliminary processes damage information before FNOL, then feeds this pre-processed data to the official claims system, reducing the processing burden and acceleration claim handling while managing external system integrations through a standardized interface
Data Source
AI summary
Apparatuses, systems, and methods are provided for the usage of enhanced pictures (e.g., photos) of tangible objects (e.g., property, cars, etc.) damaged in an accident and answers to questions about the accident to better assess the effect of the damage (e.g., repair expenses and accompanying changes to an insurance policy). A pre-FNOL system may receive responses to one or more questions regarding an accident and one or more enhanced pictures of the tangible property damaged in the accident. The pre-FNOL system may use the responses to the one or more questions and the one or more enhanced pictures to determine repair costs associated with the damaged property and accompanying changes to the insurance policy if an insurance claim were to be filed to cover the determined repairs costs.