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Automate Data Point Aggregation for SEC Filings

General Finance Use Case

An AI agent that assists the Investor Relations team (and contributing departments) by automatically gathering, validating, and consolidating specific quantitative and qualitative data points required for periodic regulatory filings (e.g., 10-K, 10-Q, 20-F, Proxy Statement sections, Sustainability Reports).

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Process Details

Inputs

  • Regulatory filing checklist with specific data points and their definitions
  • Mapping of data points to source systems and reports
  • Predefined validation rules and formatting templates

Outputs

  • Consolidated dataset of required information for the regulatory filing, populated in a designated template or system
  • Exception reports detailing missing data, validation failures, or significant variances

Systems

Describe it in English.
It runs deterministically.

This use case solution follows these general steps at a high level.

  • 01
    A checklist of required data points for a specific filing (e.g., MD&A figures, segment data, executive compensation table inputs, shareholder information, ESG metrics).
  • 02
    1. Financial Data: Accesses the ERP System to extract audited/reviewed financial figures, segment performance data, and specific note disclosure inputs.
    2. Operational Metrics: Connects to CRM Systems (e.g., Salesforce) to pull Key Performance Indicators (KPIs) relevant to the business narrative (e.g., subscriber numbers, production volumes, customer acquisition costs).
    3. HR & Compensation Data: Gather data for executive compensation tables or stock ownership details.
    4. ESG Data: Retrieves environmental, social, and governance metrics from specialized ESG Management Software, spreadsheets, or departmental databases (e.g., energy consumption, employee diversity stats, governance metrics).
    5. Shareholder Information: Accesses data from Transfer Agent Portals/Files or Shareholder Analytics Platforms for beneficial ownership information or stock composition summaries.
  • 03
    Aggregates the extracted and validated data into a centralized location or directly populates predefined templates, ensuring data is in the correct format required for the filing.
  • 04
    1. Identifies missing data points, data that fails validation checks, or significant variances that require human investigation.
    2. Generates an exception report for the IR team and other stakeholders

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Frequently Asked Questions

The agent is a versatile data aggregator that uses the best connection method for each source system:
Direct System Integration: It uses secure APIs and database connectors to pull structured data directly from enterprise systems like SAP, Oracle, Salesforce, and Workday.
Intelligent Document Processing: It can extract specific, targeted data points from designated PDFs or spreadsheets stored in secure locations like SharePoint or network drives.
The agent can be configured to perform several types of validation:
Reconciliation: Tying numbers back to a control total (e.g., ensuring the sum of segment revenues equals the total revenue reported in the ERP).
Variance Analysis: Flagging any data point that has changed by more than a predefined percentage from the prior period or budget, highlighting it for human investigation.
Completeness Checks: Ensuring that all required data points for a specific table or disclosure have been successfully retrieved.
Yes. Instead of just dumping raw data into a folder, the agent can be customized and configured to populate your team's existing, pre-formatted templates in Microsoft Word or Excel.
When the agent encounters an issue, it generates a clear, actionable exception report. This report details:
Missing Data: Which required data points could not be found.
Failed Validations: Which data points were found but failed a check (e.g., a variance was too large).
Source/Access Issues: Any systems the agent was unable to connect to.
The customization and configuration typically take 2-3 weeks. It involves:
Digitize your filing checklist, identifying every required data point.
Map each data point to its source system and specific location (e.g., report name, table, cell).
Define the validation rules and variance thresholds.
Configure the output templates.
Adding a new data requirement is a simple customization and configuration change. Your team can add the new metric to the data checklist, map it to its source, and define its validation rules. With minor customization, the agent will then include this new data point in its next run.

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