AI and machine learning models are increasingly embedded in financial reporting, audit processes, and management decision-making, but most organizations have no governance framework for the outputs they produce.

• Apply the CPA Control Framework for AI covering data quality gates, assumption documentation, output validation, and professional accountability to evaluate AI-assisted financial deliverables. 
• Assess an organization's AI governance posture against the OSFI E-23 Model Risk Management standard and Canada's Bill C-27 obligations, identifying gaps and priority remediation steps. 
• Identify and document professional liability exposure arising from AI-assisted outputs, using the Air Canada precedent and the CPA Code of Professional Conduct as the governance standard. 

Module 01 — The Governance Gap  

• Why AI adoption is outpacing governance in most organizations 
• What regulators and courts are asking: OSFI E-23, Bill C-27, Air Canada ruling 
• The CPA's professional accountability for AI outputs: Code of Professional Conduct obligations 
• The four governance obligations: Data, Assumptions, Outputs, Accountability 

Module 02 — Model Risk Framework 

• What makes an AI model high-risk in a financial context 
• Model validation: what to check and how to document 
• The interpretability test: glass box vs. black box, when each is acceptable 
• Hallucination, bias, and data quality: the three failure modes that create liability 
• The LLM usage framework for CPAs: Green / Yellow / Red

Module 03 — Building the AI Governance Audit Trail  

•  Data source documentation and financial classification taxonomy 
• Assumption logging: what must be documented and why 
• Prompt documentation for LLM-assisted work: the non-negotiable work file entry 
• Output review protocol: plausibility testing, outlier checks, source verification 
• Sign-off standards: what a defensible sign-off looks like 

• Workshop: evaluate a sample AI governance audit trail against the standard 

Module 04 — Professional Liability and Ethics  

• Air Canada chatbot ruling (Moffatt v. Air Canada, 2024): full case analysis 
• AI bias and fairness: the CPA's obligation to identify and flag discriminatory outputs 
• Explainability as a professional standard: if you can't explain it, you can't sign off 
• Scenario analysis: three governance failure cases and the professional consequences 
• Building a governance culture: how to raise AI governance concerns in your organization 

Live webinars have varying levels of expected interaction, with some requiring microphone and camera capabilities to support a proper learning experience – please email pdreg@bccpa.ca if you have concerns. 

In general, we encourage participants to have their cameras turned on to enhance their virtual learning experience. This practice promotes a stronger connection with the instructor and fellow participants, and fosters improved communication and collaboration.

Live Webinar FAQs can be found here.  

Registration terms and conditions, including the cancellation policy can be found here. If you require further assistance, please contact the PD Department.