Insurance — Pricing Document Generation Automation

Overview

The primary purpose of this DAG is to automate the creation of pricing documents within the insurance sector, ensuring improved accuracy and compliance. It integrates data from various sources, including internal databases and external market APIs, to provide a comprehensive view of pricing factors. The ingestion pipeline begins with data extraction from these sources, followed by the processing steps that involve calculating premiums based on historical claims and market trends. The generated d

The primary purpose of this DAG is to automate the creation of pricing documents within the insurance sector, ensuring improved accuracy and compliance. It integrates data from various sources, including internal databases and external market APIs, to provide a comprehensive view of pricing factors. The ingestion pipeline begins with data extraction from these sources, followed by the processing steps that involve calculating premiums based on historical claims and market trends. The generated documents are formatted in both Excel and PDF, catering to different stakeholder needs. Quality control measures are implemented throughout the process, including compliance checks and traceability audits to ensure the integrity of the data and calculations. Key performance indicators (KPIs) such as pricing accuracy and processing time are monitored to assess the effectiveness of the automation. In case of any failures during the workflow, a recovery process is initiated to maintain operational continuity. This automation not only streamlines document generation but also enhances decision-making capabilities by providing timely and accurate pricing information, ultimately driving business value through improved efficiency and reduced operational risks.

Part of the Document Automation solution for the Insurance industry.

Use cases

• Increased accuracy in pricing calculations
• Reduced time spent on manual document generation
• Enhanced compliance with regulatory standards
• Improved decision-making through timely data availability
• Lower operational risks due to automated quality controls