Scotiabank’s 27th Annual Financials Summit
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National Bank of Canada (NA) Scotiabank’s 27th Annual Financials Summit summary

Event summary combining transcript, slides, and related documents.

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Scotiabank’s 27th Annual Financials Summit summary

9 Sep, 2026

Economic and industry outlook

  • Canadian economy expected to benefit from government-led reindustrialization, defense spending, and nation-building initiatives over the next few years.

  • Trade tensions with the U.S. are causing anxiety and negatively impacting investment and labor markets.

  • Quebec consumers show strong balance sheets, low debt-to-income ratios, and high savings rates, supporting regional growth.

  • Inter-provincial barriers, especially in labor mobility and tax competitiveness, are seen as major obstacles to further economic progress.

  • Calls for tax reforms, such as recycling capital gains for reinvestment, to enhance business competitiveness.

Strategic positioning and business performance

  • Record performance in Capital Markets over the last three quarters, with balanced revenue across business lines.

  • Focus on building Corporate Investment Banking in mining, energy, infrastructure, and utilities, with profitable growth.

  • Strategy emphasizes resilience and readiness for both nation-building opportunities and market volatility.

  • Capital allocation targets a CET1 ratio of 13% by end of 2027, with a preference for growth over share buybacks.

  • Targeting ROE of 17%+ for 2027, maintaining strong capital levels to support economic growth.

AI and technology integration

  • AI is embedded as a business tool to accelerate internal processes and client experience, not as a standalone strategy.

  • Call center volumes reduced by 43% through technology, and process times cut by up to 90% in some areas.

  • Strict guardrails prevent AI from making pricing or capital allocation decisions; human oversight remains essential.

  • 3,500 technologists are equipped with agentic AI, speeding up development across the organization.

  • AI costs are managed by matching model complexity to business needs, avoiding unnecessary expense.

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