Royal Bank of Canada

Modern analytics deepen 17M customer relationships by creating relevant financial services to ensure clients thrive.

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Client expectations are shifting from casual, tailored greetings and the occasional timely offer to a world where customer experience deepens relationships, prioritizing the customer’s interests before the organization.

Royal Bank of Canada (RBC) is one of North America’s leading diversified financial services companies, providing personal and commercial banking, wealth management, insurance, investor, and capital markets products and services on a global basis. They are not only one of Canada’s biggest banks, but are among the largest in the world (ranked #256 in global Fortune 500 in 2019).

To be among the world’s most trusted and successful financial institutions, RBC puts the client first, always, to ensure clients thrive and communities prosper. 

They do so with the help of data and analytics to create exceptional customer experiences, staying connected with their customers, and reimagining the role the bank plays in their lives. With millions of customers, transforming customer experiences and deepening relationships requires bringing data together to fuel AI engines that drive decision making. Project teams are dedicated to enriching the analytical ecosystem, which RBC aptly refers to as DNA (Data & Analytics).

RBC by the numbers

32.6B
total revenue
(FY2019)
17M
clients
1,400+
countries
36
branches
Ahmed El-Kays

Ahmed El-Kays Senior Director, Data Architecture

Ahmad El-Kays’ responsibilities at RBC include enterprise data and analytics architecture, enterprise data modeling, enterprise data analysis and meta data engineering. Over the last couple of years, Ahmad has been tasked to build the RBC data knowledge teams and currently leads an organization of over 60 data and analytics experts. Prior to his current role, Ahmad worked at Scotiabank’s enterprise architecture groups and RBC’s enterprise information management. He holds a M.Sc. in Computer Science from McGill University in Montreal.

Automating processes for improved decision-making.

“The purpose for the next few years is around every client interaction to be informed by AI, which is using AI to serve our clients according to what their needs are, not according to what we think their needs are.”

Ahmed El-Kays, Senior Director, Data Architecture Royal Bank of Canada (RBC)

Lines of business relying on analytics in Teradata Vantage include:

Personal & commercial banking

Wealth management

Insurance

Anti-money laundering/fraud

CRM systems

Branches

Call center

Sales

For organizations as large and sophisticated as RBC, enabling a modern enterprise analytics ecosystem requires multidimensional scalability to support business-critical activities.

Through scalable query concurrency, complexity, data volume, sophisticated schemas, data freshness, and mixed workloads, leading banks of the future will demand hyperscale performance across all dimensions, not just a single scalable dimension. 

RBC’s data orchestration treats customers as a segment of one

“The business needs to serve the clients the insights and give them relevant offers on the spot, offering the insight or the advice as you're doing the transaction. For example, as you're on our website looking for a mortgage, we want to service you in a way that's relevant to you rather than what's relevant to the bank.”

With the help of a Teradata's modern analytics platform performing at enterprise scale, RBC remains an enterprise that will dominate into the future.

RBC is automating decision-making to transform the customer experience and deepen relationships with its 17M clients. The bank continues to unlock the imagination and insights of its people and partners to create even greater value for its clients, and the communities where they operate. 

“DNA has the richest data set in the bank that's both in our data warehouse and data lake. By using real-time data, we can offer the business a number of data services that is relevant to what they're trying to do. We launched a recommender engine based on machine learning and leveraging the data that we have, building models around it to offer our commercial account managers the right information to offer to their clients. Up until now, it was whatever they thought is relevant, rather than whatever they know is relevant.”

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