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Extraction and Monitoring SOS Registry Data for Financial Compliance

SOS Data Solution Case Study 

Problem Statement

A client with a focus on internal audits and financial compliance needed a solution to extract business registration data from Secretary of State (SOS) websites across 5 to 10 U.S. states. The data extraction was crucial for their compliance, financial auditing processes, and internal reporting. The client required data on business licenses, ownership details, and the total number of registered companies from various state databases.

Our solution

Managing data extraction across different SOS websites, each with unique access methods, posed a challenge. To overcome this, a tailored Robotic Process Automation (RPA) solution was developed.

  • Scraping Framework Development: A customized RPA-based crawler framework was built to handle the various data extraction methods used by different states. For California, full web scraping was required, while in Delaware, the system allowed for exporting data in CSV format after a user input.
  • Data Aggregation and Verification: The RPA framework aggregated business data from each state’s SOS website, ensuring the data was accurate and reliable. Different approaches for different states were implemented to handle their distinct portals and export functionalities.
  • Data Normalization: Scripts were developed to normalize and deduplicate the extracted data, ensuring consistency. This provided a standardized output across the various states.
  • Real-time Updates: The framework was designed for monthly updates to ensure continuous monitoring of changes in the SOS databases. Any changes to the website structure were also flagged for necessary adjustments to the automation.

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    Benefits

    1. Increased Accuracy: Achieved 97% accuracy in the extraction of SOS data, providing reliable information for audits and compliance processes.
    2. Time Efficiency: Reduced manual data collection efforts by 65%, allowing the client to access updated business registration data faster.
    3. Scalability: The RPA framework allowed easy expansion to additional states, adapting to new portals and export methods with minimal configuration changes.

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