Automated Document Intake System Using ML Classification
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Solution Overview
Problem
Manual processing of large volumes of unlabeled and uncategorized documents in insurance companies is time-consuming, labor-intensive, and prone to errors, leading to increased claim processing times and risks of misplaced or mis-categorized documents.
Innovation Solution
An automated document intake and processing system that uses machine learning models to categorize documents, extract relevant information, and associate it with corresponding fields in a claim processing system, thereby streamlining document management and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processing is used to categorize and extract information from documents, then document processing can be performed with simple systems, but processing time and labor requirements increase significantly
Solution Approach 1:
The patent replaces manual mechanical processing of documents with an automated system comprising an ML model for categorization and an NLP model for information extraction. The processor automatically receives documents, determines categories using the ML model, extracts relevant information using the NLP model, and populates claim fields without human intervention, thereby dramatically increasing processing speed and reducing time loss.
2Reliability
If manual categorization and information entry is performed, then system complexity remains low, but labor intensity and error risk increase
Solution Approach 1:
The patent substitutes human manual categorization with a machine learning model trained on document data to automatically determine document categories. The NLP model further extracts information and maps it to appropriate claim fields. This automated approach improves reliability and accuracy by eliminating human error while accepting the necessary complexity of implementing and training these models.
3Productivity
If large volumes of documents are processed manually, then resource requirements remain manageable, but processing capacity and throughput are limited
Solution Approach 1:
The patent replaces human labor resources with an automated processing system that can handle large volumes of documents simultaneously. The processor receives multiple documents, applies the ML model for categorization, uses the NLP model for information extraction, and populates claim fields automatically, thereby dramatically increasing processing capacity without proportionally increasing labor resources.
4Productivity
If automated processing systems are implemented, then processing speed and capacity increase, but system complexity and implementation difficulty increase
Solution Approach 1:
The patent segments the document processing system into distinct functional modules: an ML model for document categorization, an NLP model for information extraction, and a processing system that coordinates these components. This segmentation allows each component to be developed, trained, and maintained independently, managing overall system complexity while achieving high processing efficiency through specialized functions.
Data Source
AI summary
Techniques for automatic intake and handling of the documents are discussed herein. A system may automatically classify a received document as image or text, and based on the classification, further process the document to determine a scene class for image documents, and a document category for a text document. In examples, the system may use trained machine-learning models for performing one or more tasks, and provide training data for training the ML models. Further, based on the document category and characteristics of the text, the system may determine and populate associated fields in insurance records with values from the document, and determine further processing actions and associated priorities.