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AI case study—A Canadian bank

Turning AI into a strategic advantage at a Canadian bank Case Study January 07, 2026 We helped a leading Canadian bank design and deploy a generative AI solution that created measurable business value while meeting strict governance, regulatory, and security requirements.

Industry

Banking and capital markets

Our role

Accelerating the use of generative AI to deliver value and drive growth

Featuring

Improved speed and quality of key finance tasks, proving value of generative AI

Situation Determining how best to use AI to create strategic business value With regulatory expectations evolving, transaction volumes increasing, and growing demand for smarter and faster financial insights for decision making, the finance department at one of Canada’s banks knew they needed to up their game. 

But the department was already juggling numerous challenges. Critical processes were resource-intensive and time-consuming. Documentation preparation was inconsistent in style and approach, requiring multiple reviews to confirm accuracy and completeness. And data extraction for reporting was complex and highly manual.

This combination of factors drove the finance department to realize they didn’t simply want to improve their existing processes. They wanted to reinvent them using generative AI capabilities that could help them streamline analytics processes, automate labour-intensive activities, improve the timeliness of content development, generate high-quality first draft content, and lay the foundation for expanding their use of AI to create value over time.

In 2024, the bank’s finance department brought our PwC Canada team on board to help with their initial AI transformation, including the implementation of three generative AI use cases to showcase how AI could be used to drive strategic business value.

Solution Accelerating the bank’s AI journey by implementing key generative AI use cases We worked with the finance department to fundamentally rethink how they could harness AI—particularly generative AI—to transform key finance processes to improve the quality and timeliness of their financial and regulatory activities and provide higher quality insights for decision making. We centered our approach around three key principles:

A focus on reinvention: We worked collaboratively with the finance department to look at how they could use generative AI capabilities and tools to transform their financial processes while maintaining appropriate controls and robust governance.

A modular, use-case oriented approach aimed at proving AI’s value: We met the finance team where they were at, helping them to prove the value of AI to the business by implementing three generative AI use cases. This modular, use-case focused approach allowed us to accelerate the bank’s ability to use generative AI in a trusted way to create strategic business value in the short term while helping them build the framework, capabilities, and governance required to expand their use of AI over time.

A multi-dimensional approach to change: We prioritized more than technology change—addressing structure, process, and people dimensions simultaneously with the technology implementation to build internal capabilities, foster buy-in, and drive user adoption.  At a more granular level, we worked collaboratively with the bank to implement generative AI for three high-priority use cases. These included: 

Developing an executive earnings assistant: We helped the finance department implement generative AI to automate the creation of analyst reports and earning transcript summaries to minimize manual work and drive more timely, accurate, and insightful information.

Automating aspects of MD&A reporting: We worked with the bank to implement AI to streamline the development of Management Discussion and Analysis commentaries in order to streamline financial reporting processes, produce consistent and accurate documents, and reduce manual effort—while maintaining strict regulatory compliance.

Enabling LLM-assisted data extraction to support peer analytics: We created a versatile generative AI-based solution able to extract and process data from a variety of financial documents so that the bank could automate and streamline peer analytics activities, decrease the need for manual intervention and verification, and reduce the risk of errors.

Results Using AI to enhance the finance function’s strategic value to the business Our initial work enabled the bank’s finance department to demonstrate the business value associated with embracing AI tools and solutions and provided them with a strong foundation for expanding their use of AI over time.

Across each generative AI use case, the finance team has realized measurable business value. Today, related business activities are completed faster, team members can focus on higher-value work thanks to automation of previously labour-intensive tasks, and outputs are more accurate and deliver deeper insights.

Since implementing these initial use cases, the finance department has continued their journey to reinvent finance operations using AI to enhance their strategic value to the business. Over the past year, we have collaborated with them to implement several additional generative AI use cases. We are now working with them to take their AI approach to the next level through the development of an agentic AI roadmap and agentic AI platform. 

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Sylvia Shah

Director, Cloud Data and AI, PwC Canada

Email

Abhinav Ravi

Partner, Cloud Data & AI / Data & AI Leader for Banking, PwC Canada

Tel: +1-514 553-6551

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Rakesh Shetty

Partner, Consulting, Finance Transformation, PwC Canada

Tel: +1 416 815 5031

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Rani Turna

Partner, Assurance, PwC Canada

Tel: +1 416 869 2406

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Vik Pant

Chief Data Scientist & Emerging Technology Leader, PwC Canada

Tel: +1 647 330 0642

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