by Glen Leppert CTO, DBRS

1. Domino and AWS: Collaborative Analytics and
Model Governance at Financial Services Firms

2. Introduction
Financial Services Industry
• Highly regulated
• Broad range of firm sizes, from small hedge funds to global
money center banks
• Large amount of analytical work related to transactions (e.g.
mergers and acquisitions, securitizations, equity IPOs)
• Subscribers to multiple data vendors
• Historically have been massive consumers of technology
Technical Challenges
• Large amounts of legacy analytics
• Subject matter experts technically proficient, but not software
engineers
• Collaborative analytics between front office desks, risk
departments and back office can get complex
• Desktop environment and version management creates
challenges for reproducibility and collaboration
• Multiple version control file storage and database systems
• Spreadsheets and shared drives that never stop growing

3. Financial Service Industry Analytics
Common Tasks
• Building predictive risk models
• Validating the predictive models (often by a different group)
• Derivative pricing
• Constructing and back testing algorithmic trading strategies
• Credit risk stress testing
• Transaction analytics
• Reconciliation / tie-out
• Data driven research

4. Financial Service Industry Analytics
Typical quantitative developer profile
• Masters/pHd in math, physics or statistics
• Deep subject matter expertise in a rather narrow specialty
• Often build and support their own analytical stack. An
example:
• High powered local machine
• Sas, R, Matlab, SPSS
• C++
• SQL
• Excel, csv
• Maintain large libraries themselves
• Some (not all) can be a bit reluctant to adopt new technologies
into their stack

5. DBRS Overview
DBRS is an internationally recognized credit rating agency that has been providing issuers,
regulators, investors and intermediaries with objective, transparent, insightful risk analysis and
opinion since 1976.
DBRS rates entities in:
Argentina
Australia
Austria
Barbados
Belgium
Brazil
Canada
Cayman Islands
Chile
China
Colombia
Cyprus
Denmark
Finland
France
Germany
Greece
India
Ireland
Italy
Japan
Luxembourg
Malta
Mexico
Netherlands
New Zealand
Norway
Peru
Portugal
Spain
Sweden
Switzerland
Turkey
UK
USA
Uruguay
DBRS employs approximately 440 professionals around the world, with offices in New
York, Chicago, Toronto, London and Mexico City.
Public Finance
(includes Sovereigns) – 39%
Corporates – 6%
Financial Institutions &
Insurance – 26%
Structured Finance – 29%
Coverage by
Industry
DBRS Overview

6. DBRS & Domino
Securitization Analysis Example – Residential Mortgage Backed Security
Investment
Decision
Predictive
Analytics
Train/test regression
model on large historical
mortgage data set
Transaction
Analytics
For a given default &
interest rate scenario,
forecast the interest and
principal paid to the bonds Qualitative
Analysis
Legal documentation
review (true sale, events
of default)
Traditional data
science (R, Python)
Fixed income
analysis (Excel,
VBA, Python)
Human group decision
making (Word,
PowerPoint, Email)

7. DBRS & Domino
Traditional Approach Modern Approach
Data exploration / wrangling
• Excel
Scripting
• VBA
Technical computing
• Stata, Matlab, R
Storage
• Shared drive, SQL Server
Model engines
• C#, VBA
Front end
• Excel add-ins
Presentation
• Powerpoint
Data exploration / wrangling
• Jupyter notebooks
Scripting
• Python
Technical computing
• Python (numpy, pandas, sci-kit), R
Storage
• S3, Athena, Domino projects
Model engines
• Python
Front end
• Excel add-ins, R Shiny
Presentation
• Plotly, Jupyter notebooks, Powerpoint*
*Still can’t escape it

8. DBRS & Domino
Model Governance Example
Development
• Parameter estimation performed in Jupyter notebooks, which
are easily shared with the model review team
• Excel add-in which allows developers dump transaction data to
json files, Domino via REST API, or S3 bucket
• Developer codes locally with their IDE, using json files to debug
• Code changes reviewed and approved via GitHub
• Domino project automatically synced to the repo
• Developer can easily setup the correct environment for their
project inside of Domino
Advantages
• Analysts interact with Excel/R Shiny front end as they normally
would, the code is all executed server side on Domino
• Analyst workflow is auditable in the form of a Domino run,
which is a record of the code version, inputs, outputs and
environment
• Scalable via AWS, large batch jobs are easily scheduled
• Developers can control their code base by hosting it server side
without any knowledge of web technology, Docker containers,
etc.
• Development can still be very rapid & flexible

9. AWS Global Infrastructure
16 Regions – 42 Availability Zones – 76 Edge Locations Region & Number of Availability Zones
AWS GovCloud (2) EU
Ireland (3)
US West Frankfurt (2)
Oregon (3) London (2)
Northern California (3)
Asia Pacific
US East Singapore (2)
N. Virginia (5), Ohio (3) Sydney (3), Tokyo (3),
Seoul (2), Mumbai (2)
Canada
Central (2) China
Beijing (2)
South America
São Paulo (3)
Announced Regions
Paris, Ningxia, Stockholm
Over 2 million active customers across 190 countries
2,300+ Government Agencies
7,000+ Educational Institutions
1,000+ Financial Services Organizations
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

10. Right Now in Asset Management...
…a top 5 asset manager is consolidating data centers, with a plan to
move 400 workloads (5,000 servers) in 2 years to AWS.
...that same industry-leading firm is shuttering its internal private cloud.
...2 of the top 10 asset managers in the world are moving “all in” to AWS.
...and migrating its order routing system off of mainframe to AWS.
...leading providers of solutions to the industry are migrating those
applications to AWS.
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

11. FS enterprises are using AWS to transform
BANKING & PAYMENTS INSURANCECAPITAL MARKETS
Capital One

12. “For our market surveillance systems, we are looking at about 40% [savings with AWS], but the real
benefits are the business benefits: We can do things that we physically weren’t able to do before,
and that is priceless.
”
RIGHT NOW: FINRA is using Amazon S3 and EMR to Power Market Surveillance
FINRA is the largest independent
regulator for all securities firms
doing business in the US. FINRA
oversees about 4,250 brokerage
firms, about 162,155 branch offices
and approximately 629,525
registered securities
representatives.
- Steve Randich, CIO, FINRA
What FINRA needed:
• Infrastructure for its market surveillance platform
• Analysis and storage of approximately 35 billion market events every day
• Interactively query multi-petabyte data sets
Why they chose AWS:
• Fulfillment of FINRA’s security requirements
• Ability to create a flexible platform using dynamic clusters (Hadoop, Hive, and
HBase), Amazon EMR, and Amazon S3
Benefits realized:
• Increased agility, speed, and cost savings
• Estimated savings of $20m annually by using AWS
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

13. “Nasdaq has been a user of Amazon Redshift since it was released and we are extremely happy with
it. Currently, our system is moving an average of 5.5 billion rows into Amazon Redshift every day.”
”
RIGHT NOW: Nasdaq is Using Amazon Redshift to Power Data Analytics
Nasdaq is the 2nd largest exchange
group in the world by market
capitalization. Nasdaq operates one
of the world’s largest networks of
exchanges, which spans 26 markets,
1 clearing house, and 5 central
securities depositories. Its markets
trade multiple asset classes
including equities, options, futures,
fixed-income, commodities,
derivatives and structured products.
- Nate Sammons, Principal Architect, Nasdaq
What Nasdaq needed:
• Replacement of on-premise legacy warehouses
• Reduction of cost and increase in data capacity
Why they chose AWS and Amazon Redshift:
• Fulfillment of security and regulatory requirements
• Cost efficiencies without sacrificing functionalities
Benefits realized:
• System that moves an average of 5.5 billion rows into Amazon Redshift every day
(with 14 billion on a peak day in Oct of 2014)
• Ability to increase accessibility of historic data to a growing number of internal
groups
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

14. AWS in Capital Markets
Broadridge has launched a technology
platform on AWS named Inlet to make it
easier for companies to provide millions
of consumers access to their most
important documents from hundreds of
providers in just a few clicks. Broadridge
uses Inlet to deliver investment industry
content for its financial institution
customers.
Global Investor Communications
Robinhood is a U.S. based financial
services company founded in 2013 that
runs its entire broker-dealer on
AWS. The Robinhood app allows
individuals to invest in publicly
traded companies and exchange-traded
funds listed on U.S. exchanges without
paying a commission.
$0 Commission Trading
Trading Technologies, a leading global
provider of a derivatives trading platform
to the financial services industry, has built
their next-generation trading platform
using a hybrid architecture, with its
backbone built on AWS spanning all
publicly available AWS regions which are
connected to co-located infrastructure at
global exchanges.
Global Trading Solutions
This global exchange group has migrated
its entire historical data footprint to
AWS, as well as an industry-leading
messaging application, and a web-based
trading platform.
Global Exchange Group
In order to achieve greater resource
scalability, St. James’s Place has migrated
85% of its applications to AWS and
expects a full migration by the end of
2016.
UK Wealth Management
TickerTags uses AWS to maintain a
curated taxonomy of over 300,000
tagged products, brands, competitors,
topics, affiliated people, places,
regulatory threats, etc. Tags serve to
bridge and filter immense volumes of
unstructured data for market insight.
Social Sentiment
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

15. Conclusions
Domino & AWS
• There is a large population of analysts who are “part time” data scientists, and are not using the optimal technology
stack. Collaborative analytical solutions enable them to get up to speed rapidly and help reduce silos
• Domino provides easy access to AWS, git, docker containers, and REST API’s for non-software engineers
• There is strong momentum for AWS, not only for large firms but small ones as well
• While the modular nature of the Python/R ecosystems provide many benefits, one must be cautious of environment,
especially package management
• Subject matter experts should focus on business logic & model design, let somebody else do the plumbing!