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Insurance — Named Entity Extraction for Insurance Documents

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This DAG extracts named entities from insurance documents to enhance data governance and compliance. By leveraging NLP models, it ensures data accuracy and facilitates efficient information retrieval.

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Overview

The purpose of this DAG is to extract named entities from various insurance documents sourced from ERP systems and CRM platforms. The ingestion pipeline begins with the collection of data from these sources, which includes policy documents, claim forms, and customer records. Once ingested, the data undergoes processing through advanced natural language processing (NLP) models that identify key information such as policy numbers, claim identifiers, and customer names. Quality control measures are

The purpose of this DAG is to extract named entities from various insurance documents sourced from ERP systems and CRM platforms. The ingestion pipeline begins with the collection of data from these sources, which includes policy documents, claim forms, and customer records. Once ingested, the data undergoes processing through advanced natural language processing (NLP) models that identify key information such as policy numbers, claim identifiers, and customer names. Quality control measures are integrated into the workflow to ensure the accuracy and reliability of the extracted data, including checks for entity recognition and validation against predefined criteria. The processed data is then stored in a centralized data warehouse, enabling easy access for further analysis and reporting. In case of any failures during the extraction process, alerts are generated to notify relevant stakeholders, ensuring timely intervention. Key performance indicators (KPIs) such as extraction accuracy rates, processing time, and failure rates are monitored to assess the effectiveness of the DAG. The business value derived from this DAG includes improved compliance with regulatory requirements, enhanced data governance, and streamlined operations in the insurance sector.

Part of the Governance & Compliance solution for the Insurance industry.

Use cases

  • Enhances compliance with industry regulations
  • Improves data accuracy for decision-making
  • Reduces manual data entry and processing time
  • Facilitates better customer insights and analytics
  • Streamlines operations and reduces operational risks

Technical Specifications

Inputs

  • • ERP transaction logs
  • • CRM customer records
  • • Insurance policy documents
  • • Claim forms
  • • Regulatory compliance documents

Outputs

  • • Extracted named entities dataset
  • • Quality assurance reports
  • • Alert notifications for failures
  • • Data warehouse entries
  • • Compliance documentation

Processing Steps

  1. 1. Ingest data from ERP and CRM systems
  2. 2. Preprocess documents for NLP analysis
  3. 3. Apply NLP models to extract named entities
  4. 4. Conduct quality control checks on extracted data
  5. 5. Store validated data in the data warehouse
  6. 6. Generate alerts for any processing failures
  7. 7. Monitor KPIs for ongoing performance assessment

Additional Information

DAG ID

WK-1208

Last Updated

2025-12-01

Downloads

96

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