Governed AI for financial services.
Adopt AI across advice, operations, and client service while meeting privacy law and the model risk expectations your regulator and your clients apply.
- Coverage
- PIPEDA · Law 25 · OSFI E-23
- Frameworks
- ISO 42001 · NIST AI RMF
- Delivery
- Bilingual, senior led
The problem
Financial firms hold some of the most sensitive personal data in the country, and AI is entering the workflow through tools nobody formally approved: meeting summarizers, drafting assistants, research copilots, and embedded features inside existing platforms. The exposure is twofold: privacy law on the data, and model risk on the decisions.
What we do
- Inventory AI tools and models in use, with risk tiering by client impact
- Map data flows, residency, and cross border exposure for client financial data
- Assess against PIPEDA, Law 25, and, for federally regulated institutions, OSFI Guideline E-23 model risk expectations
- Set human oversight on any decision that affects a client
- Build monitoring, vendor due diligence, and an AI incident process
OSFI Guideline E-23, in plain terms
E-23 asks federally regulated institutions to know which models they run, to document how each was built and validated, to assign accountable owners, and to monitor performance over time. Applied to AI, that means model documentation, a validation record, defined human oversight, and drift monitoring with thresholds. We build that record so it holds up under review.
Your sensitive data stays yours
We build AI adoption so client financial data never trains a public model. Every automation carries a mandatory human checkpoint. We work from encrypted, secured devices, carry cyber liability coverage, and can sign a mutual confidentiality agreement before any engagement begins.
Common questions
- Does OSFI Guideline E-23 apply to us?
- E-23 sets model risk management expectations for federally regulated financial institutions, with explicit coverage of AI and machine learning models. Provincially regulated firms are not bound by it, but many adopt it because clients and partners increasingly expect that level of rigour.
- What does governed AI look like in a finance setting?
- A model and tool inventory with risk tiering, documented model development and validation, human oversight on decisions that affect clients, monitoring for drift and bias, vendor due diligence on third party AI, and an incident process for harmful or incorrect output.
- We are a small advisory firm, is this overkill?
- No. We scale the program to the size of the firm. For a small practice the priority is knowing which tools are safe for client financial data, setting retention and residency correctly, and having a written policy and training record.
Your next enterprise contract is waiting on this.
- Flat fee, total cost known up front
- Canadian data residency available on paid engagements