Automated Insurance Claim Auditing via OCR and NLP
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems lack the capability to automate the review, approval, and payment process for insurance claims due to the inability to convert imaged claim documents into a usable electronic form, leading to inefficiencies and inflated costs, with manual audits being time-consuming and only partially effective in ensuring accuracy and adherence to best practices.
Innovation Solution
A computerized auditing method that translates word strings from claim documents into translated item descriptions, compares them to predetermined terms, and associates them with item identifiers based on matching information, allowing for automated acceptance or rejection of claim items according to predefined rules, thereby generating audit reports and multi-point estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual auditing is performed to ensure accuracy of claim estimates, then claim quality and accuracy are improved, but review time and costs increase significantly
Solution Approach 1:
The patent replaces manual mechanical auditing with an automated electronic auditing system that uses optical character recognition (OCR) to convert imaged documents into machine-readable data, natural language processing to understand claim descriptions, and automated rule engines to evaluate claims against insurance guidelines. This substitution maintains auditing accuracy while dramatically reducing review time from days to minutes.
Solution Approach 2:
The patent introduces an intermediary automated auditing system that acts as a bridge between manual claim submission and final approval. This intermediary system pre-processes claims using OCR and natural language processing, flags potential issues, and prepares standardized outputs, thereby reducing the time burden on human auditors while maintaining thorough review standards.
2Reliability
If imaged documents are used for claim estimates, then document authenticity and completeness are preserved, but automation of review and approval processes becomes technically impossible
Solution Approach 1:
The patent replaces the mechanical limitation of processing only imaged documents with optical character recognition (OCR) technology that converts images into machine-readable text. This substitution enables automated processing while preserving the original imaged documents for authenticity verification, allowing the system to maintain document reliability while achieving full process automation.
Solution Approach 2:
The patent creates digital copies of imaged claim documents through OCR technology. These copies are machine-readable versions that can be processed automatically while the original imaged documents are retained for verification purposes. This copying approach enables automation without compromising document authenticity.
3Productivity
If electronic auditing tools are used to flag possible issues, then initial screening efficiency is improved, but complete automation cannot be achieved and manual audit is still required
Solution Approach 1:
The patent implements a feedback loop where the automated auditing system continuously learns from manual audit outcomes. The system analyzes patterns in flagged issues, refines its natural language processing rules, and updates its evaluation algorithms based on auditor feedback. This feedback mechanism enables the system to progressively improve its accuracy, eventually achieving complete automation without manual intervention.
Solution Approach 2:
The patent enables the auditing system to perform self-improvement through machine learning algorithms that automatically analyze claim patterns, identify emerging fraud indicators, and refine evaluation criteria without human intervention. This self-service capability allows the system to evolve from simple flagging to comprehensive automated auditing.
4Reliability
If appraisers select higher priced OEM parts on repair estimates, then part quality and availability are improved, but repair costs and insurance rates increase
Solution Approach 1:
The patent implements feedback mechanisms that monitor part selection patterns, pricing trends, and repair outcomes. The system compares OEM part selections against alternative parts, analyzes actual repair quality data, and provides feedback to appraisers and repairers. This feedback loop enables the system to identify when premium OEM parts are unnecessarily selected, allowing for cost optimization while maintaining quality standards.
Solution Approach 2:
The patent introduces dynamic decision-making capabilities that allow the system to adaptively select between OEM and alternative parts based on real-time factors such as part availability, repair urgency, vehicle usage patterns, and cost-benefit analysis. This dynamic approach replaces static preference for OEM parts with flexible, context-aware part selection that optimizes both quality and cost.
Data Source
AI summary
Described are computer-based methods and apparatuses, including computer program products, for automation of auditing claims. A data file comprising make model information, insurance company information, and one or more auditable items, each auditable item comprising a word string having one or more words. The make model information is automatically translated into a vehicle identifier. The insurance company information is automatically translated into an insurance company identifier. The one or more auditable items are automatically translated into one or more groups of identifiers, wherein the one or more groups of identifiers comprises one or more part item identifiers, one or more operational item identifiers, or both. A parts audit report is automatically generated based on the one or more groups of identifiers. An operational audit report is automatically generated. A multi-point estimate is automatically generated based on the parts audit report data and the operational audit report data.