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AI-Assisted Name Screening for Corporate Filings

Enforcement

What it collects that can identify you

Sensitive personal information
Identifiable data
  • Structured name fields from Individuals with Significant Control filings submitted to Corporations Canada. These fields contain the legal names of identified individuals associated with corporate entities.

Also collects operational data, which is anonymized data.

Run by
Innovation, Science and Economic Development Canada (ISED)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Accountable organization
Your copy
You cannot see the data it holds about you. What you can do

What it is for

Enforcement

This tool helps federal government staff review names submitted in Individuals with Significant Control filings by automatically flagging names that may be inappropriate, non-genuine, or offensive. It uses machine learning and natural language processing to identify anomalies for human review — no administrative decision is made by the system itself. If your name was flagged, a staff member will review it before any action is taken.

What it collects and what happens to it

Data taken in

Sensitive personal information
Identifiable data
  • Structured name fields from Individuals with Significant Control filings submitted to Corporations Canada. These fields contain the legal names of identified individuals associated with corporate entities.
Operational data
Anonymized data
  • The internal Corporations Canada Individuals with Significant Control filings dataset, comprising structured records of corporate filing submissions used to train and operate the name-analysis model.

Processing

Anomaly Detection
  • Machine learning model that detects anomalies or inappropriate content in name fields — identifying names that depart from expected patterns for genuine individual names, including those that may be offensive or non-genuine.
Language Models
  • Natural language processing techniques are used to analyse the linguistic content and semantic properties of name strings submitted in corporate filings.

What it does

Deciding (Analytical AI)
Human decides
  • The system scores and flags name fields from corporate filings as potentially inappropriate, non-genuine, or offensive. A human staff member reviews all flagged cases before any action is taken; the system does not make administrative decisions.
Sensing (Perceptive AI)
Human decides
  • Uses natural language processing to read and interpret structured name fields from corporate filing records, transforming raw text into signals that the analytical flagging layer can assess.

Outputs

A recommendation or prediction
Identifiable data
  • A flag indicating that a specific name field in an Individuals with Significant Control filing may be inappropriate, non-genuine, or offensive. The flag is advisory — it routes the record to a staff member for human review and does not constitute an administrative decision.

Run by

Innovation, Science and Economic Development Canada (ISED)
  • Corporations Canada, a branch of Innovation, Science and Economic Development Canada, developed and operates ISCAN as an internal tool to support post-filing reviews of Individuals with Significant Control filings.

Government of Canada AI Register — ISCAN

Built by

Not stated by the Helpful Places.

Kept for

Not stated by the Helpful Places.

Shared with

Not available to me
  • ISCAN is an internal government tool used by GC employees only. The system's outputs (flags) are not accessible to the individuals whose names were assessed.
Available to the accountable organization
  • Flagged records and system outputs are available to Corporations Canada staff for the purpose of conducting post-filing reviews.

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 UseCorporations Canada discloses AI use to users. Individuals who have submitted filings containing their names should be aware that an AI system may review those names for compliance purposes. The AI Register entry for this system is publicly accessible at the Government of Canada Open Data portal.
  • Right to a Human ReviewAll cases flagged by the system are reviewed by Corporations Canada staff before any action is taken. The system does not make administrative decisions autonomously; a human decision-maker is involved in every consequential outcome.

Risks and safeguards

  • Reputational harmThe system may incorrectly flag a legitimate name as inappropriate or offensive, potentially exposing an individual to unwarranted scrutiny or delays in corporate filing processing.Safeguard: The system does not make administrative decisions; all flagged cases are reviewed by staff before any action is taken, limiting the likelihood of direct harm from a false positive.
  • Civil liberties harmA model trained on historical name data may disproportionately flag names from certain cultural, linguistic, or ethnic backgrounds as anomalous, introducing systemic bias against particular communities.Safeguard: Human review of all flagged cases is the primary safeguard; however, the register entry does not describe bias testing, fairness audits, or demographic monitoring of flag rates.