Targeted Event Monitoring With Personalized Loss Mitigation
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Solution Overview
Problem
Catastrophic event preparedness is often inadequate, leading to high damage costs and loss of life, while the insurance industry is reactive and prone to fraud, with inefficient manual investigative processes.
Innovation Solution
A computing system that integrates machine learning and AI to provide individualized loss mitigation services, predictive event severity analysis, and real-time fraud detection, using interactive user interfaces for preparedness, FNOL information gathering, and claim processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If general preparedness guidelines are provided to individuals, then ease of operation is improved, but loss of life and damage costs increase due to inadequate collective mitigation
Solution Approach 1:
The patent segments preparedness guidance into individualized portions based on each policyholder's specific risk factors, property characteristics, and predicted event severity. Instead of providing generic guidelines to all individuals, the system divides and tailors preparedness content to specific subsets of users based on their unique circumstances and predicted exposure to catastrophic events.
Solution Approach 2:
The system applies local quality by providing customized preparedness content with different levels of detail and specificity to different individuals based on their localized risk profiles. Each policyholder receives preparedness guidance tailored to their specific property characteristics, geographic location, and predicted event severity, rather than uniform generalities applicable to all.
2Ease of operation
If the insurance industry uses manual investigative processes, then ease of operation is maintained for investigators, but productivity decreases and fraud increases
Solution Approach 1:
The patent replaces manual mechanical investigative processes with automated electronic systems including AI-powered image analysis, telematics data processing, and automated fraud detection algorithms. These electronic systems analyze claim information, process images of damaged property, evaluate telematics data, and identify fraudulent patterns without requiring manual investigator intervention for routine analyses, thereby dramatically improving productivity while maintaining operational simplicity through standardized automated workflows.
3Measurement precision
If fraud detection is performed manually after claim events, then measurement precision can be maintained through human judgment, but loss of time increases enabling perpetrators to cover their tracks
Solution Approach 1:
The system performs preliminary fraud detection actions by continuously monitoring and analyzing data before claim events occur. Telematics devices continuously track vehicle usage patterns, and the system maintains baseline profiles of normal behavior. When claims are filed, the system compares claim information against pre-established baselines and cross-references with other data sources in real-time, enabling immediate fraud detection rather than delayed post-claim investigation.
Solution Approach 2:
The system implements continuous feedback loops where claim information, image analysis results, telematics data, and fraud detection outcomes are fed back into the system to refine fraud detection algorithms and update risk profiles. This real-time feedback mechanism allows the system to learn from each claim and improve detection accuracy progressively, maintaining high measurement precision while operating at automated speeds.
4Reliability
If individualized preparedness guidance is provided, then loss mitigation effectiveness is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal platform that handles multiple functions within a single integrated system: risk assessment, event prediction, personalized content generation, multi-channel delivery, and feedback collection. This multi-functional approach consolidates what would otherwise require separate complex systems into one unified platform, achieving individualized preparedness guidance while managing overall system complexity through integration rather than proliferation of separate components.
Data Source
AI summary
A computing system can receive monitoring data from the one or more monitoring services. Based on the monitoring data, the system can determine that an event will occur in a given area and determine a subset of users within the given area. For each respective user of the subset of users, the system generates interactive content for display on a computing device of the respective user, the interactive content providing the respective user with contextual information regarding the event and a set of mitigative actions. The computing system executes a machine learning model comprising an engagement monitor to adapt the interactive content for the respective user.