AI Claim Processing System for Fraud Detection and Loss Mitigation
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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 patent replaces manual investigative processes with automated machine learning models and AI systems that analyze claim data, detect patterns, and identify fraudulent behavior. This substitution maintains fraud detection capability while dramatically improving processing speed and efficiency, allowing the system to analyze claims in real-time rather than allowing perpetrators to cover their tracks.
Solution Approach 2:
The system performs preliminary fraud detection and analysis before claims are fully processed. By pre-screening claims using machine learning models that analyze historical data and patterns, the system identifies potentially fraudulent claims early in the process, allowing investigators to focus only on high-risk cases and improving overall processing efficiency.
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 patent implements continuous feedback loops where machine learning models analyze claim outcomes, fraud detection results, and loss data to continuously improve their predictive capabilities. This feedback mechanism allows the system to adapt to emerging fraud patterns and improve detection accuracy over time, reducing fraud losses and preventing premium increases while maintaining simple claim processing for legitimate cases.
Solution Approach 2:
The system replaces reactive manual claim processing with proactive automated analysis using machine learning and AI. This substitution enables the system to predict and prevent fraud before claims are filed, reducing overall fraud rates and premium costs while maintaining ease of operation for legitimate claims through automated processing.
3Adaptability or versatility
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 patent provides localized, individualized preparedness guidance based on each property's specific characteristics, location, and risk factors. Rather than generic advice, the system analyzes historical data, property features, and environmental factors to generate customized mitigation recommendations for each property, making the guidance highly adaptable while significantly reducing potential damage costs through targeted prevention measures.
Solution Approach 2:
The system performs preliminary risk assessment and generates individualized preparedness plans before catastrophic events occur. By analyzing historical data, property characteristics, and environmental factors in advance, the system provides proactive mitigation guidance that enables property owners to take preventive actions before disasters strike, reducing damage costs while maintaining adaptability to local conditions.
Data Source
AI summary
Subsequent to a claim event, a computing system can connect with a plurality of third-party data sources to obtain information pertaining to at least one of loss or damage resulting from the claim event. Based on the information from the plurality of third-party sources, the system determines a set of policy holders that have been impacted by the claim event. The system then transmits an electronic communication to a computing device of each policy holder in the set of policy holders to initiate a claim process for the policy holder.