Insurance — Claims Data Extraction and Taxonomy Update Pipeline

Overview

The purpose of this DAG is to streamline the extraction of data from claims documents, enabling the continuous updating of the taxonomy used in claims analysis. The architecture involves a series of well-defined steps that begin with the ingestion of claims documents, including PDFs and structured data files. The data pipeline processes these inputs through normalization, where inconsistencies are resolved, followed by validation to ensure data accuracy and completeness. This is crucial for main

The purpose of this DAG is to streamline the extraction of data from claims documents, enabling the continuous updating of the taxonomy used in claims analysis. The architecture involves a series of well-defined steps that begin with the ingestion of claims documents, including PDFs and structured data files. The data pipeline processes these inputs through normalization, where inconsistencies are resolved, followed by validation to ensure data accuracy and completeness. This is crucial for maintaining high-quality data that informs the claims management system. The outputs of this workflow include an updated taxonomy and validated claims data, which are then fed into the claims management system to facilitate better decision-making. Key performance indicators (KPIs) for monitoring the effectiveness of this DAG include the taxonomy update rate and the data extraction time. Additionally, a recovery process is triggered in case of failures, ensuring that the workflow remains resilient. The overall business value lies in improved claims processing efficiency, reduced operational costs, and enhanced customer satisfaction through timely and accurate claims management.

Part of the Predictive Maintenance solution for the Insurance industry.

Use cases

• Increased efficiency in claims processing operations
• Enhanced accuracy in claims analysis and decision-making
• Reduced time spent on manual data entry and validation
• Improved customer satisfaction through faster claims resolution
• Cost savings from streamlined data management processes