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AI Assistant for Financial Accounting Policies

Inform · Financial Services

What it collects

Operational data
Anonymized data
  • Unclassified financial accounting policy and procedure documents stored in Microsoft SharePoint, including policy files, procedure files, cheat sheets, and attestation documents in PDF and Word formats, in both English and French.
About behaviour
Anonymized data
  • Natural language queries submitted by GC employees to the chatbot, used at runtime to interpret user intent and retrieve relevant policy sections.
Run by
Global Affairs Canada (GAC)
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

InformFinancial Services

This AI-powered chatbot helps Global Affairs Canada staff quickly find answers about financial accounting and procurement policies stored in internal documents. It uses a large language model to interpret plain-language questions in English or French and returns relevant excerpts with source citations. The system does not process personal information, and users are informed they are interacting with an AI tool.

What it collects and what happens to it

Data taken in

Operational data
Anonymized data
  • Unclassified financial accounting policy and procedure documents stored in Microsoft SharePoint, including policy files, procedure files, cheat sheets, and attestation documents in PDF and Word formats, in both English and French.
About behaviour
Anonymized data
  • Natural language queries submitted by GC employees to the chatbot, used at runtime to interpret user intent and retrieve relevant policy sections.

Processing

Language Models
  • Large Language Models (LLMs) accessed via the OpenAI API are used to interpret natural language queries, detect input language (English or French), and generate bilingual responses with document citations.
Search & Retrieval
  • The system searches across up to 20 unclassified SharePoint documents to find and surface relevant policy sections in response to user queries, returning results with active source URLs.

What it does

Understanding (Semantic AI)
Human decides
  • The system interprets natural language queries, searches across up to 20 SharePoint documents, and surfaces relevant policy sections with citations. Staff members review and act on the retrieved information themselves.
Creating (Generative AI)
Human decides
  • The chatbot generates responses in natural language (English or French), composing answers that synthesize content from retrieved policy documents and include active source URLs.

Outputs

Generated content
Anonymized data
  • Natural language responses in English or French that synthesize relevant policy excerpts and include document citations with active URLs, generated by the LLM in response to staff queries.
A recommendation or prediction
Anonymized data
  • The chatbot surfaces relevant policy sections and suggests applicable procedures, acting as an advisory tool — final interpretation and action remains with the staff member.

Run by

Global Affairs Canada (GAC)
  • Global Affairs Canada is the federal department that deploys and operates this AI chatbot to support staff access to financial accounting and procurement policy information.

Government of Canada AI Register — 2526-GAC-AMC-020

Built by

Government of Canada / OpenAI
  • The chatbot was developed by the Government of Canada and uses the OpenAI API to provide large language model capabilities for natural language interpretation and response generation.

Government of Canada AI Register — 2526-GAC-AMC-020

Kept for

Not stated by the Helpful Places.

Shared with

Available to the accountable organization
  • Query logs and interaction data, if retained, are accessible to Global Affairs Canada administrators. The system is restricted to GC employees and not available to the general public.
Not available to me
  • Members of the public do not have access to this system or its outputs. The chatbot is an internal tool for GC employees only.

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 UseUsers are informed that they are interacting with an AI tool before using the chatbot. The system discloses its AI nature at the point of use, as confirmed in the official register entry.
  • Right to Algorithmic TransparencyStaff are informed of the general logic of the chatbot: it uses LLMs via the OpenAI API to search SharePoint policy documents and generate cited responses. The AI Register entry is publicly accessible.

Risks and safeguards

  • Societal & cultural harmLLM-generated responses may contain hallucinations or misrepresent policy content, potentially leading staff to act on incorrect information.Safeguard: Responses include citations with active source URLs, enabling staff to verify answers against the original documents. The tool is advisory; final decisions rest with trained staff.
  • Loss of autonomyOver-reliance on the chatbot may reduce staff engagement with original policy documents, eroding policy expertise over time.Safeguard: The chatbot is designed as a time-saving tool, not a replacement for expert judgment; it surfaces source documents and encourages staff to consult them. Experts are expected to focus on complex tasks.