Automated Claim Processing System Using NLP and AI
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current claim processing procedures, such as those for insurance claims, require significant manual effort and are prone to errors, lacking accuracy and transparency due to the need for manual handling of structured and unstructured data.
Innovation Solution
The implementation of a system utilizing natural language processing (NLP), image processing, and machine learning techniques to extract and process claim information from various documents, converting unstructured data into structured form, and applying policy rules to determine benefit eligibility and payment amounts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual procedures are used for claim processing, then flexibility in handling complex cases is maintained, but processing speed and productivity are significantly reduced
Solution Approach 1:
The system enables self-service automation where the claim processing system automatically extracts information from documents, determines eligibility, calculates benefits, and generates decisions without requiring manual intervention for routine claims. The system serves itself by using AI and NLP to perform tasks previously requiring human agents, thereby increasing productivity while reducing manual handling.
2Measurement precision
If manual claim processing is used, then complex judgment can be applied to edge cases, but accuracy and consistency are reduced due to human error
Solution Approach 1:
The patent replaces the mechanical human decision-making process with an automated system using AI, NLP, and machine learning models. This substitution eliminates human errors in data extraction, eligibility determination, and benefit calculation, thereby improving measurement precision and accuracy while maintaining ease of operation through automated processing.
3Reliability
If comprehensive document analysis is performed to ensure accurate claim assessment, then measurement precision improves, but processing time and complexity increase
Solution Approach 1:
The system segments the claim processing into distinct modular components: document ingestion, information extraction using NLP, eligibility determination, benefit calculation, and decision generation. Each module handles a specific aspect of processing, allowing comprehensive document analysis to be performed systematically while managing complexity through modular architecture and specialized AI models for each segment.
4Productivity
If automated processing is implemented to increase productivity, then processing speed improves, but handling of unstructured data becomes more difficult
Solution Approach 1:
The system uses advanced NLP and AI techniques to substitute manual analysis of unstructured data with automated information extraction. The system can process unstructured documents such as policy papers, claim forms, and supporting evidence, extracting relevant information automatically to maintain high productivity while overcoming the difficulty of handling unstructured data formats.
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, claim data, and customer and policy data, in conjunction with automatic requests for human intervention. Policy rules, benefit calculation formulae, necessary data points, and benefit requirements are extracted from policy documents using NLP and AI techniques. 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 against the policy rules for eligibility assessment and benefit calculation formulae to generate a benefit payment amount and entitled additional benefits.