Automated Contextual Flow Dispatch for Insurance Claim Corroboration
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Solution Overview
Problem
Catastrophic event preparedness is inadequate, leading to high damage costs and loss of life, and the insurance industry is plagued by inefficiencies and rampant fraud due to reactive and manual claim processing methods.
Innovation Solution
A computing system that integrates machine learning, artificial intelligence, and data augmentation to provide predictive loss prevention and mitigation services, automate claim processing, and detect fraud by leveraging real-time data and historical information for policy holders and providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual investigative processes are used for claim processing, then investigators can identify fraudulent behavior, but the process is inefficient and allows perpetrators to cover their tracks
Solution Approach 1:
The system performs preliminary investigative actions by automatically gathering contextual information, analyzing data patterns, and identifying potential fraud indicators before claims are formally processed. This proactive approach allows the system to detect fraudulent behavior early when evidence is still available, preventing perpetrators from covering their tracks while maintaining efficient processing through automation.
Solution Approach 2:
The patent replaces manual mechanical investigative processes with automated computational systems that use machine learning, data analytics, and contextual information gathering. This substitution maintains or improves fraud detection accuracy while dramatically increasing processing efficiency by eliminating manual review bottlenecks and enabling parallel analysis of multiple claims simultaneously.
2Ease of operation
If reactive claim processing is used, then insurance companies can process claims after events occur, but fraud increases and premium costs rise
Solution Approach 1:
The system transitions from reactive to proactive processing by automatically gathering contextual information and analyzing claims in real-time as they are filed. This preliminary analysis detects fraud indicators immediately, preventing fraudulent claims from being processed while maintaining simple operations through automated decision-support tools that guide investigators only when necessary.
Solution Approach 2:
The system implements continuous feedback loops where claim data, contextual information, and fraud detection results are analyzed to improve future claim processing. This feedback mechanism enables the system to learn from patterns, refine fraud detection algorithms, and adapt to emerging fraud schemes, thereby reducing fraud while maintaining operational simplicity through automated adjustments.
3Ease of operation
If general preparedness guidance is provided, then individuals receive basic information, but damage costs remain high and individualized mitigation is inadequate
Solution Approach 1:
The system transitions from uniform general preparedness guidance to localized, individualized recommendations tailored to each policyholder's specific risk profile, property characteristics, and historical data. This local quality approach provides customized mitigation strategies that address specific vulnerabilities, thereby reducing damage costs while maintaining ease of access through automated personalized guidance delivery.
Solution Approach 2:
The system provides preliminary preparedness guidance before catastrophic events occur by analyzing risk factors, property data, and historical patterns to generate proactive mitigation recommendations. This advance preparation enables policyholders to take preventive actions that reduce potential damage costs while maintaining simple, accessible guidance through automated systems that deliver personalized recommendations.
Data Source
AI summary
A computing system can receive input about a claim event from a first party. The system may then initiate a process for obtaining information about the claim event by providing a series of prompts to the first party to obtain information about the claim event from the first party, identifying a second party to provide information about the claim event based on the information provided by the first party, providing a series of prompts to the second party to obtain information about the claim event from the second party, and determining one or more actions for completing the process based on the information provided by the first party and the second party.