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AI-Assisted Data Quality Verification for Financial Statistics

Planning & Decision-making · Enforcement

What it collects

Operational data
Anonymized data
  • Financial and project management records drawn from the Project Management System, Financial Management System, and GC_MASTER_DATA. These are government administrative datasets with no personal information.
Run by
Global Affairs Canada (GAC)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Accountable organization

What it is for

Planning & Decision-makingEnforcement

This tool uses algorithms, regression models, and data science techniques to automate quality-assurance checks on financial and project data managed by Global Affairs Canada's Statistical and Reporting Unit. It compares observed values against expected values, flags discrepancies, and generates QA status reports to help staff catch data entry errors more quickly. The system processes government operational data only — no personal information is involved. It is currently in development and used exclusively by government employees.

What it collects and what happens to it

Data taken in

Operational data
Anonymized data
  • Financial and project management records drawn from the Project Management System, Financial Management System, and GC_MASTER_DATA. These are government administrative datasets with no personal information.

Processing

Classification & Prediction
  • Regression models and statistical algorithms compare observed data values to expected values, classifying records as within tolerance or flagging them as potential errors for human follow-up.

What it does

Deciding (Analytical AI)
Human decides
  • Uses algorithms and regression models to check for differences between observed and expected values, assign QA priority, and prepare status reports. Human staff review flagged issues and decide on corrective action.

Outputs

A recommendation or prediction
Anonymized data
  • Produces QA status reports, prioritized lists of records requiring review, and signals flagging discrepancies to data OPIs. Outputs are advisory — human staff make final decisions on corrections.

Run by

Global Affairs Canada (GAC)
  • Global Affairs Canada's CFO Statistical and Reporting Unit (SWS) deploys this tool to improve its back-end data quality assurance process for grants and contributions financial reporting.

Government of Canada AI Register — SWS-QA-Tool (2526-GAC-AMC-013)

Built by

Government of Canada
  • The system was developed internally by the Government of Canada, not by an external vendor.

Government of Canada AI Register — SWS-QA-Tool (2526-GAC-AMC-013)

Kept for

Not stated by the Helpful Places.

Shared with

Available to the accountable organization
  • QA outputs and reports are available to Global Affairs Canada's CFO Statistical and Reporting Unit staff and data OPIs. The register indicates AI use is not disclosed to end users, though the primary users are GC employees.

Stored

Not stated by the Helpful Places.

How to read the colours

Can it identify you?

Anonymized data
Data about people with the link to who is broken. Stripped of identifiers, blurred, aggregated, or noised so this system can’t reasonably tie a record back to an individual.
Pseudonymous data
Each person’s data is tied to a token (hash, ID, template) that lets this system recognise the same person across events, but the token itself doesn’t reveal a name. Reidentification is possible with extra information.
Identifiable data
The data either contains a direct identifier (name, address, account name, recognisable face or voice, plate number) or carries a token this system uses to look up legal identity during processing.

Who completes the loop?

Human decides
This mode suggests; a person decides what to do next. The AI is always advisory — a human is in the loop on every decision. Example: a triage tool ranks cases for a clinician who chooses which to see first.
Human executes
This mode decides; a person carries out the result. Example: an optimizer plans the day’s trash-collection routes, and drivers run them.
Autonomous
This mode decides and acts on its own. No person reviews each decision or carries out the resulting action.

Definitions from the DTPR standard. Amber is about your data, violet about who decides. The fuller the shape and the deeper the colour, the more identifying the data or the less a person is involved.

What you can do

Ask about this system

Questions go to the Helpful Places, not the vendor.

Your rights

  • Right to Be Informed of AI UseThe AI register records that AI use is not currently disclosed to users of this system. As the system is used exclusively by GC employees processing internal government data (no personal information), the register does not describe a formal notice mechanism for external individuals.

Risks and safeguards

  • Reputational harmThe system may incorrectly flag valid data records as errors, potentially causing unnecessary rework or misrepresenting the quality of grants and contributions data.Safeguard: Outputs are advisory only — human staff review all flagged issues before action is taken. The system is in development and replaces an existing SAP QA tool, allowing for comparative validation.