Veracity Analyzer for Insurance Claims Fraud Detection
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Solution Overview
Problem
Current computing devices are unable to effectively detect false or unreliable statements made by humans, which can lead to inaccuracies in insurance claims and increased costs for innocent customers, as they lack the capability to identify indicators of inaccuracy or deception in human statements.
Innovation Solution
A veracity analyzer (VA) computing device using artificial intelligence and machine learning techniques generates models from historical statements to identify reference indicators of inaccuracy, parses current statements to detect candidate indicators, and flags potentially false statements, enabling real-time alerts and reducing reliance on human input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human review of statements is used, then accuracy of statement verification is improved, but productivity and processing speed deteriorate
Solution Approach 1:
The patent introduces an intermediary system (veracity analyzer with AI/ML models) that acts as a mediator between human reviewers and statements. This intermediary pre-analyzes statements, identifies potential inaccuracies, and prioritizes cases for human review, thereby maintaining high verification accuracy while significantly improving processing throughput by filtering out clearly accurate statements before they reach human reviewers.
Solution Approach 2:
The system performs preliminary analysis of statements using trained AI/ML models before human review. By pre-identifying indicators of inaccuracy and pre-screening statements, the system prepares cases in advance, allowing human reviewers to focus only on complex or suspicious cases, thus improving both accuracy and productivity.
2Productivity
If no verification system is used, then productivity is improved, but reliability of statements deteriorates
Solution Approach 1:
The system enables statements to essentially self-verify through automated AI/ML analysis. The veracity analyzer independently evaluates statements using trained models that detect indicators of inaccuracy, providing automated verification that maintains reliability while improving productivity by reducing dependency on manual review for all cases.
Solution Approach 2:
The patent replaces the purely mechanical human review process with an automated electronic verification system using AI/ML algorithms. This substitution maintains or improves reliability through consistent application of verification criteria while dramatically improving productivity by automating the verification process.
3Productivity
If automated analysis is implemented, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The verification process is segmented into multiple stages: automated AI/ML analysis for initial screening, followed by selective human review for complex cases. This segmentation allows the system to maintain high productivity through automation while preserving measurement precision by involving human reviewers when needed, creating a hybrid approach that leverages the strengths of both automated and manual processes.
Solution Approach 2:
The system uses trained AI/ML models that have learned optimal parameters for detecting indicators of inaccuracy from historical data. By adjusting and optimizing these parameters based on training data, the system improves the accuracy of automated detection, reducing false positives and negatives, thereby maintaining measurement precision while achieving high productivity.
Data Source
AI summary
The present embodiments may relate to secondary systems that verify potential fraud or the absence thereof. Artificial intelligence and/or chatbots may be employed to verify veracity of statements used in connection with insurance or loan applications, and/or insurance claims. For instance, a veracity analyzer (VA) computing device includes a processor in communication with a memory device, and may be configured to: (1) generate at least one model by analyzing a plurality of historical statements to identify a plurality of reference indicators correlating to inaccuracy of a historical statement; (2) receive a data stream corresponding to a current statement; (3) parse the data stream using the at least one model to identify at least one candidate indicator included in the current statement matching at least one of the plurality of reference indicators; and/or (4) flag, in response to identifying the at least one candidate indicator, the current statement as potentially false.