TD Bank, North America’s 10th largest bank, has embarked on a digital transformation journey from a finance company into a tech company. The bank is aiming to deliver amazing customer service and improved efficiencies via big data, data lakes, Hadoop, artificial intelligence and machine learning.

1. The Amazing Ways TD Bank, Canada’s
Second-Largest Bank, Uses Big Data, AI
& Machine Learning

2. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
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IntroductionIntroduction
TD Bank is Canada’s second largest bank, and the 10th largest bank in North
America. Its focus is on retail banking and it employs over 26,000 people.
The bank provides its services to around 25 million customers, and its guiding
philosophy is “legendary customer service”. In short this means that every
interaction with the bank should be a memorable and enjoyable experience.
The Amazing Ways TD Bank, Canada’s Second-Largest
Bank, Uses Big Data, AI & Machine Learning

3. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
Title
Text
IntroductionIntroduction
Over the last five years TD Bank’s Enterprise Information Management team has
rolled out the “Googleficaton” of the business.
Essentially this has meant transforming it from a finance company into a tech
company – and managing the cultural shift that this entails.
The Amazing Ways TD Bank, Canada’s Second-Largest
Bank, Uses Big Data, AI & Machine Learning

4. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
A Big Data Environment
It started with an idea that moving to a Big Data environment could bring
about a 50-fold reduction in costs over the relational database infrastructure
which had previously been in place. A tall order, on the face of it, but one that
started with a fairly simple premise.
IT doesn’t do anything in a business that couldn’t already be done by someone
else – so the sole reason for it existing at all is to enable things to be done
better, faster and cheaper than it could be done before.
By moving to a data lake infrastructure, and switching to providing data-as-a-
service functions, TD Bank effectively democratized access to the information it
gathers and stores as part of its business. These include transactional records
and customer service interactions – enabling it to act far more quickly on data-
driven insights.

5. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
A Big Data Environment
The first part of this was getting all of the data into one place where it could be
used together – the data lake. But simply throwing all of an organization –
particularly a bank’s – information together isn’t a simple process. The data
needs to be in a state where it can be quickly found and used by those who
need it, but of course there are obligations around access and data security
too.
In fact, the whole point of a data lake is to make the data accessible and usable
across an organization, rather than it being compartmentalized in “data silos”
where its usefulness is restricted, usually to those who collected it in the first
place. But if you aren’t careful how you go about it, the end result could more
closely resemble a data swamp than a data lake!

6. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
Information Management
This was overcome by the information management team by breaking down
the essential information about information – the metadata – that needed to
be recorded to make the data useful.
The team established that this came down to:
1. What is the data?
2. Who can access it?
3. Under what circumstances can they access it?
Once these tags were filled in, the team knew that data could be loaded into
the lake and would always be in a suitable format to be found and used.

7. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
Information Management
Data gathered by TD Bank into its lake included data on customer behavior,
personal data such as their interests, internal and external data, in both
structured and unstructured forms. This unstructured data includes audio and
video recordings of customer interactions with the bank.

8. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
Accessibility
The next step was to set out what problems this newly accessible and available
data can be used for. A decision was made at this stage to go for “quick wins”.
These are mission-critical objectives where it can be quickly shown that
building a Big Data infrastructure can pay off – generating more savings
through driving efficiencies, than the infrastructure costs to deploy.
TD Bank’s Hadoop private cloud was built around Cloudera’s solution, and the
bank looked to Talend as its analytics partner, to build services enabling value
to be extracted from the data anywhere in the business where it could be put
to use.
The infrastructure was built around open source tools such as Hive, Impala,
Spark and Tableau which enable querying of data and output of reports and
visualizations, alongside the Talend framework.

9. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
“Configuration Not Coding”
Throughout this deployment, the team aimed to create an experience
dependent on “configuration not coding”. To get at the insights, the bank’s
employees should simply have to set up the software using the parameters
relevant to their particular task – rather than code solutions from scratch.
This enabled the development of tools which meant the bank can act on the
wealth of data it holds on its customers in order to offer them tailor-made
services.
For example, if the bank knows that a customer is in the process of a major life
event such as buying a house, marrying or having a child, this data informs the
products and services they might be offered.

10. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
This is done by rolling out what it describes as a “BI (business intelligence) in
Hadoop” strategy. By deploying this technology, it says it has reduced the
operating cost by a factor of around 50 times, per gigabyte of data processed.
The infrastructure has also made it possible to create customer-centric digital
services such as its MySpend app, which allows customers to track their
monthly spending. Insights derived from the aggregated data created by
millions of customers is used to offer suggestions which can help improve
individual spending habits.
“Configuration Not Coding”

11. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
Predicting Customer Needs
This capability to act on data-driven insights received a boost with the
acquisition this year of Toronto machine learning experts Layer 6. This
investment in smart, self-learning technology will help it build systems which
can more accurately predict customer needs.
Machine learning is being increasingly adopted throughout financial services
for its ability to accurately predict customer needs, provide personalised
product and service recommendations, anticipate complaints and power
chatbots to provide a smoother feedback experience.
TD Bank had already shown its commitment to using automation to improve
customer experience through its Twitter chatbot and its adoption of Amazon’s
Alexa devices to offer voice banking to customers.

12. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
Predicting Customer Needs
It may at first glance seem counter-intuitive that customers will feel that they
are treated more like individuals, as more and more of their data becomes
aggregated and processed by machines.
But the philosophy is that smarter machines which can “know” customers
better will make customers feel as if they have returned to an earlier age, when
they would expect personal service from a bank manager who would know
their name, and understand what is important to them.

13. © 2017 Bernard Marr , Bernard Marr & Co. All rights reserved
© 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a
strategic business & technology advisor to governments and companies. He helps
organisations improve their business performance, use data more intelligently, and
understand the implications of new technologies such as artificial intelligence, big data,
blockchains, and the Internet of Things.
LinkedIn has ranked Bernard as one of the world’s top 5 business influencers. He is a frequent
contributor to the World Economic Forum and writes a regular column for Forbes. Every day
Bernard actively engages his 1.5 million social media followers and shares content that
reaches millions of readers.
Visit The
Website
© 2017 Bernard Marr , Bernard Marr & Co. All rights reserved
© 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a
strategic business & technology advisor to governments and companies. He helps
organisations improve their business performance, use data more intelligently, and
understand the implications of new technologies such as artificial intelligence, big data,
blockchains, and the Internet of Things.
LinkedIn has ranked Bernard as one of the world’s top 5 business influencers. He is a frequent
contributor to the World Economic Forum and writes a regular column for Forbes. Every day
Bernard actively engages his 1.5 million social media followers and shares content that
reaches millions of readers.
Visit The
Website

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