Claims Processing System Using ML Document Classification
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Solution Overview
Problem
Traditional insurance claims processing is inefficient, relying on adjusters for documentation gathering, which is time-consuming and can be improved with the use of smart technologies like smartphones for quicker data collection and processing.
Innovation Solution
A claims processing system leveraging machine learning models to classify documents, determine workflows, and assist users in documenting and sharing information, enabling efficient data processing and analysis on user devices and cloud servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional adjusters manually gather documentation, then claims processing can be thorough, but the process is time-consuming and inefficient
Solution Approach 1:
The system enables customers to self-serve by taking photos and videos of damage and accident scenes using their own smartphones, eliminating the need for adjusters to manually collect documentation. The machine learning model automatically processes these customer-submitted media files to extract claim information, further reducing manual intervention time.
Solution Approach 2:
The patent replaces the mechanical process of manual documentation gathering with automated machine learning-based processing. The machine learning model automatically analyzes images and videos submitted by customers, extracting relevant claim information without human intervention, thus significantly reducing processing time.
2Productivity
If customers use smartphones to take photos and videos, then data collection becomes quicker and more efficient, but the system complexity increases
Solution Approach 1:
The machine learning model serves as an intermediary between the customer's smartphone and the claims processing system. It receives media files from the smartphone, processes them to extract claim information, and transfers the structured data to the claims system, thereby simplifying the overall system architecture while maintaining high data collection speed.
3Extent of automation
If machine learning models are used to classify documents, then workflow determination becomes automated and faster, but the initial setup and training requirements increase complexity
Solution Approach 1:
The machine learning model is pre-trained using historical claims data and documentation before deployment. This preliminary training allows the model to automatically classify documents and determine workflows without requiring complex setup or training during actual claim processing, thus achieving high automation while minimizing operational complexity.
Data Source
AI summary
A claims processing system includes a claims assistance component to handle the flow of data through the claim process. The data may include any documents submitted for claim processing and may be generated by disparate sources. The system may train one or more machine learning (ML) models to classify the unstructured data by insurance categories and/or determine workflow. The system may assist a user through a user device to upload or capture claims data with the device. The claims assistance component may classify the data and verify the data classification before uploading it to the servers. In examples, the claims assistance component may determine that the file size of the data exceeds a cellular data transfer threshold and determine to compress the data and/or hold the transfer until a free connection is detected.