Open-and-Closed Claims Models for Insurance Loss Estimation
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Solution Overview
Problem
Conventional systems struggle to efficiently and accurately process large volumes of complex insurance documents due to their disparate formats and the need for human intervention, leading to inefficiencies in data extraction and analysis.
Innovation Solution
A system utilizing multiple OCR engines and machine learning models to enhance data processing, incorporating human-in-the-loop tasks for quality assurance, enables efficient and accurate extraction and analysis of insurance documents, including loss history estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple OCR engines are used to process documents, then data accuracy is improved, but processing time and system complexity increase
Solution Approach 1:
The patent segments the document processing task by dividing it into multiple parallel OCR processing streams. Each stream processes the same document with a different OCR engine simultaneously, rather than sequentially. This segmentation allows the system to leverage multiple engines for improved accuracy while maintaining efficient processing throughput.
Solution Approach 2:
The patent merges the outputs from multiple OCR engines through a consensus mechanism. The system combines results from different engines and uses voting or agreement algorithms to determine the most accurate transcription, thereby improving data accuracy while managing the complexity of integrating multiple processing sources.
2Reliability
If multiple OCR engines are deployed, then OCR errors are minimized, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary consensus layer that mediates between multiple OCR engines. This intermediary component receives outputs from various engines, reconciles differences through comparison and voting mechanisms, and produces a unified accurate result. This mediator manages the complexity by providing a standardized interface that abstracts the underlying multi-engine complexity.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously compares outputs from multiple OCR engines and uses disagreement detection to trigger reprocessing or human review. This feedback loop improves reliability by identifying and correcting errors while managing complexity through automated conflict resolution protocols.
3Measurement precision
If human-in-the-loop tasks are incorporated for quality assurance, then data quality is improved, but processing speed decreases
Solution Approach 1:
The patent applies partial human intervention by routing only a subset of documents to human reviewers based on confidence scores and error detection. Documents that pass automated quality checks with high confidence are processed automatically, while only those with low confidence or detected errors are escalated to human reviewers. This partial action maintains high processing speed while improving data quality through targeted human oversight.
Data Source
AI summary
Techniques including a system and method for analyzing insurance claims. The techniques may involve the use of a system. The system may include a plurality of machine learning models trained on a plurality of insurance loss claim records, the plurality of machine learning models comprising: at least one machine learning model trained on open claims records; and at least one machine learning model trained on closed claims records. The system may include a component configured to predict a loss for a subject insurance claim based on outputs of the at least one machine learning model trained on open claims records and the at least one machine learning model trained on closed claims records.