Automated Claim Processing System Using AI and NLP
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Solution Overview
Problem
Current claim processing procedures, such as those for insurance claims, require significant manual effort and are prone to errors, lacking automation, accuracy, and transparency in handling structured and unstructured data.
Innovation Solution
The system employs natural language processing, information extraction, and AI techniques to convert unstructured claim data into structured form, extracting relevant information and applying policy rules and benefit calculation formulae to determine payment amounts, thereby automating the claim processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual procedures are used for claim processing, then flexibility in handling complex cases is maintained, but processing time and error rates increase significantly
Solution Approach 1:
The patent replaces manual mechanical processing with an automated system combining optical character recognition (OCR), natural language processing (NLP), and rule-based engines. The system automatically extracts data from unstructured claim documents, structures it, applies policy rules, and generates payment decisions without human intervention for standard claims, thereby reducing processing time while maintaining accuracy through systematic validation.
Solution Approach 2:
The patent introduces an intermediary processing layer between claim submission and final payment that includes document conversion engines, information extraction modules, and rule application layers. This intermediary system pre-processes claims, validates data completeness, and prepares structured outputs for final approval, reducing the time burden on manual reviewers while ensuring accuracy through multiple validation checkpoints.
2Reliability
If manual claim processing is used, then complex judgment calls can be made, but consistency and transparency across different claims decrease
Solution Approach 1:
The patent creates a universal processing framework that handles multiple claim types (insurance, warranty, rebate, return claims) through a single integrated system. The rule engine is designed to be policy-agnostic, allowing different policies and terms to be processed through the same automated workflow, ensuring consistency across diverse claim types while managing complexity through modular design.
Solution Approach 2:
The patent transforms unstructured document parameters into structured data parameters through OCR and NLP conversion. By standardizing input parameters (extracting specific data fields from varied document formats) and output parameters (uniform payment decisions), the system achieves consistency across different claim types while managing complexity through parameter standardization and validation rules.
3Measurement precision
If all available data is processed manually, then comprehensive accuracy can be achieved, but the cost and time required for processing increase
Solution Approach 1:
The patent applies partial automation where the system automatically processes claims that meet predefined criteria for completeness and clarity, while flagging only those claims requiring manual review. This selective approach allows high-volume standard claims to be processed rapidly with full data extraction accuracy, while maintaining the option for human review on complex cases, thereby increasing overall throughput without sacrificing precision on automated claims.
Solution Approach 2:
The patent performs preliminary data extraction, validation, and structuring automatically before claims reach manual reviewers. The system pre-processes documents, extracts relevant information, validates against policy rules, and prepares draft decisions in advance. This preliminary action reduces the workload on manual processors to only review and approve pre-processed claims, increasing throughput while maintaining accuracy through early validation.
Data Source
AI summary
Methods, systems and apparatuses, including computer programs encoded on computer storage media, are provided for processing claims using both unstructured and structured policy documents and claim data. Policy rules, benefit calculation formulae, necessary data points, and benefit requirements are extracted from policy documents. Unstructured claim data is converted to a structured form using natural language processing, information extraction, and AI techniques to identify and extract relevant information, including values for the data points and benefit conditions, then the combined structured data and converted unstructured data is processed to get all values for the data points and applicable benefit conditions. The relevant claim information is then further processed according to the policy rules and benefit calculation formulae to generate a benefit payment amount and entitled additional benefits.