ATB Financial has partnered with the University of Alberta to build a real-time artificial intelligence system. Current research projects include using reinforcement learning to detect unhappy customers before they churn, and using RL to optimize the online customer experience. The goal is to utilize cutting-edge techniques to analyze data streams in real-time, identify changes in customer behavior, and take action. An early prototype was built using Google Cloud Platform. Next steps include connecting to real data streams, developing reusable components, and strengthening partnerships with academia.

1. Collaborating with University of
Alberta to Build a Real-Time
Artificial Intelligence System
Gunjan Kaur
Enterprise Data Science
ATB Financial
May 16, 2018, Lunchalytics

2. Background
Gunjan (Senior Analytics Manager)
● Economics and Finance (UofA)
● 4 Years (ATB - Enterprise Data
Science, Credit risk)
● Other experience - FX modelling
2
“Our role is simple - to transform the world of banking, exploit the power of AI and machine learning
to create new insights that will drive better solutions for our customers and create an exceptional
understanding of their needs.”

3. 3
Partnership Between ATB Financial and
University of Alberta
● ATB signed a multi-year research partnership with
UAlberta
● Goal: To push and test the boundaries of research by
connecting a community of diverse backgrounds across
science, mathematics, and economics and bring this
research to production quickly to solve problems for ATB
customers
https://ai.atb.com/

4. 4
Strategy - Why?
● ATB’s vision statement
● Create happiness
● Provide exceptional banking services - something you do
for customers, not to customers
● Leverage new tools and technology to deliver outstanding
experiences for team members and customers
● To lead the way in applications of AI and machine learning
to make banking, and life, better for Albertans

5. 5
Current Research: Churn Early Warning
● Goal: apply reinforcement learning to customer
behaviour data to detect unhappy customers
and remedy the situation before they leave ATB
● Comprehensive, non black box churn model
that gives understanding to the underlying
mechanism of churn at ATB
● Dr. Chris Frei (prof. Math. and Stats. Sciences
UofA), Bohdan Horak, Joshua Mitra (grad
students UofA)
● Mark Sebestyen (product manager, Data
Scientist, ATB)

6. 6
● Goal: use state of the art reinforcement learning
models to create the best possible online customer
experience
● Borrow game theoretic RL advancements and
solutions to the banking context, become the decision
engine of atb.com and ATB mobile banking platforms,
e.g. NextBestConversation
● Dustin Morrill (Graduate student, Comp.Sci. UofA)
● Mark Sebestyen (Product Manager, Data Scientist,
ATB)
Current Research: Customer Experience RL Lab

7. Current Research: Real-Time Artificial
Intelligence System
7
● Goal: Utilize cutting edge techniques to
analyze data streams in real time,
identify and act upon changes and
anomalies in customer behaviour in real
time
● U of A Researchers: Dr. Omid
Ardakanian, Dr. Hamzeh Khazaei
● ATB staff: Team of ATB data scientists
and developers
What is Normal ?

8. Problem Statement: Why ?
● Current state: Advanced analytics based on batch updated/static data
● Derivation of greater insights from stream data vs summarized
data/calculations
$%^^^^^^^^$##$$$$$$$@@@@^*&&&&&& vs average spend = $$$
● Reusable components of real time analytics infrastructure between different
use cases:
Fraud/System monitoring
Churn/customer servicing needs
Account management
● Establish a fusion platform - set of interfaces that will enable ATB and UofA
team members created components to co-exist and interact seamlessly
8

9. Early Prototype in Google Cloud Platform
● Goal: Provision of a
flexible, portable and
adaptable point of
departure for UofA team
research
● Why Google Cloud
Platform ?
Cloud based infrastructure
with auto scaling capabilities -
virtual machines, tensorflow
for ML, storage, querying and
streaming capabilities
9
Simulated Data
Data
Preprocessing,
Model Deployment
Push/Pull,
Subscribers
Querying, Final
output, Real Time
Dashboard

10. Next Steps and Key Learnings
● Connect to real-time data streams from point-of-sale systems
● Microservices - containerization
● Microservices - creation of seamless interface for interaction between
different components
● Amend the prototype to various use cases: Account Monitoring, Customer
Servicing (different data streams, ML algo)
● Build out research network further - strengthen partnerships with academia
● Cost savings from quick prototyping of PoC, rapid deployment of solutions
10

11. 11
Follow Our Transformation
Journey:
https://atbalphabeta.com/

12. Questions
12
Please connect with us if you’re interested in learning more about Data Science at ATB !
Dmitriy Volinskiy - dvolinskiy@atb.com
Tyler Dauphinee - tdauphinee@atb.com
Gunjan Kaur - gkaur2@atb.com