ML Model for Automated Property Hazard Assessment
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Solution Overview
Problem
Existing methods for assessing and insuring external plots of land are inefficient due to the tedious and time-intensive process of extracting image-based plot properties from digital images, leading to potential mistakes and excessive resource allocation, which can result in unnecessarily granted policies.
Innovation Solution
A computing entity uses trained machine learning models to extract image-based plot properties from digital images on a pixel-by-pixel basis, generating predictions for policy transactions and hazard scores, thereby reducing the need for in-person inspections and improving accuracy in predicting policy transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual methods are used to extract image-based plot properties from digital images, then assessment accuracy can be maintained through human judgment, but the process becomes tedious and time-intensive, leading to excessive resource allocation and potential mistakes
Solution Approach 1:
The patent replaces manual mechanical inspection with automated machine learning models that process digital images to extract plot properties. The system uses trained ML models to automatically identify hazards, assess risks, and generate property assessments without human intervention, thereby eliminating the tedious and time-intensive nature of manual extraction while maintaining or improving accuracy through consistent algorithmic application.
2Reliability
If more computing resources are allocated to improve prediction accuracy, then policy transaction prediction becomes more reliable, but the computing time and resource consumption increase
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on extensive datasets of property images and hazard patterns before deployment. This pre-training phase, while computationally intensive, is performed once beforehand, allowing the models to make accurate predictions with reduced computing resources during actual policy evaluation. The models are prepared in advance to efficiently process new images without requiring excessive real-time computing power.
3Measurement precision
If traditional inspection methods are used, then thorough hazard identification can be achieved, but in-person inspections are required which increase resource allocation and time consumption
Solution Approach 1:
The patent uses copying by creating digital replicas of physical properties through photographing and imaging. Instead of requiring in-person physical inspections, the system captures digital images of properties and uses machine learning models to analyze these copies for hazard identification. This approach maintains thorough hazard detection capabilities while eliminating the need for physical presence, thereby reducing operational complexity and resource allocation.
Data Source
AI summary
Embodiments of the present disclosure provide methods, systems, apparatuses, and computer program products for programmatically predicting policy transactions using machine learning.