Predictive Crowd-Sourced Risk Analysis for Insurance Loss Control
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Solution Overview
Problem
The insurance industry faces challenges in determining the cause of losses due to the limitations of using photographs taken after an occurrence, which are often costly and ineffective in assessing high-risk activities before a loss occurs, leading to a need for a system that can preemptively identify actuarially significant circumstances and prevent losses through advance loss control measures.
Innovation Solution
A computer-implemented system that compiles and assesses crowd-sourced data, including images, text, and video, to detect actuarially significant circumstances, correlates this data with in-force insurance policies, and issues electronic communications to policyholders for loss control and additional coverage offers, enabling proactive risk management and prevention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If photographs are taken after a loss has occurred to assess the cause of loss, then the insurance representative can make determinations on the cause of loss, but the assessment is too late to accurately determine what actually caused the loss and requires costly on-site visits
Solution Approach 1:
The system performs preliminary assessment by analyzing crowd-sourced photographs and data before a loss occurs. Insurance representatives can identify potential risks and high-risk activities in advance, allowing them to take preventive actions or adjust coverage before the actual loss event happens, thereby improving the accuracy and timing of loss cause determination.
2Reliability
If insurance representatives conduct on-site visits to damaged properties to assess loss, then they can gather firsthand information, but each visit is costly to the insurer
Solution Approach 1:
The system uses crowd-sourced photographs and remote data as copies of the actual property conditions, eliminating the need for physical on-site visits. These digital copies provide sufficient information for accurate loss assessment, thereby maintaining assessment quality while eliminating the costly energy expenditure of travel and on-site inspection.
Solution Approach 2:
The system replaces the mechanical process of physical on-site visits with an automated digital analysis system that processes crowd-sourced photographs and data. This substitution maintains or improves assessment reliability while eliminating the costs associated with human inspectors traveling to locations.
3Loss of information
If traditional claim evaluation methods are used that are backward looking, then insurers can assess losses after they occur, but they fail to assess potentially high risk activities policyholders engage in before the loss occurs
Solution Approach 1:
The system performs preliminary risk assessment by analyzing crowd-sourced data before losses occur. It identifies high-risk activities and conditions in advance, allowing insurers to provide loss control recommendations or adjust coverage before the actual loss event, thereby capturing information about risky behavior that traditional backward-looking methods miss.
Solution Approach 2:
The system establishes a continuous feedback loop by monitoring crowd-sourced data in real-time. When high-risk activities are detected, the system can notify policyholders and insurers, allowing for immediate corrective actions. This feedback mechanism transforms the traditional reactive claims process into a proactive risk management system.
Data Source
AI summary
A crowd sourced based predictive system for detecting and analyzing actuarially significant activity, including high risk activities that may result in damage to an insured property, alerts users of the potential conditions and activities in order to assist with loss control and provide users with the ability to purchase insurance coverages relates to the conditions and activities.