Insurance — Named Entity Recognition for Client and Claim Data Enrichment

Overview

The purpose of this DAG is to extract named entities from client and claim documents, thereby enriching existing databases with critical information. By analyzing various data sources such as policy documents, claim reports, and customer communications, the DAG identifies key entities that can provide insights into customer behavior and claim trends. The ingestion pipeline begins with the collection of unstructured text data from the specified sources. The processing steps include natural langua

The purpose of this DAG is to extract named entities from client and claim documents, thereby enriching existing databases with critical information. By analyzing various data sources such as policy documents, claim reports, and customer communications, the DAG identifies key entities that can provide insights into customer behavior and claim trends. The ingestion pipeline begins with the collection of unstructured text data from the specified sources. The processing steps include natural language processing (NLP) techniques that identify and categorize named entities, such as names, dates, and monetary values. Quality control measures are implemented at each stage to ensure the accuracy and reliability of the extracted data, including validation checks and error logging. The outputs of this DAG consist of enriched databases that include detailed entity information, which can be utilized in downstream applications like pricing models and risk assessments. Monitoring key performance indicators (KPIs) such as the extraction success rate and processing time helps maintain the efficiency and effectiveness of the workflow. The business value of this DAG lies in its ability to enhance data quality, improve decision-making processes, and ultimately optimize pricing strategies in the insurance sector.

Part of the Pricing Optimization solution for the Insurance industry.

Use cases

• Improves data accuracy for better risk assessment
• Enhances customer insights for tailored pricing strategies
• Reduces manual data entry and processing time
• Facilitates compliance with regulatory data standards
• Increases operational efficiency through automated workflows