Moody’s Analytics offers unique tools for measuring and managing risk through expertise and experience in credit analysis, economic research, and financial risk management. In this presentation, Senior Director of Software Engineering Marcelo Schnettler discusses the benefits of running EDF (Expected Default Frequency) 9 in the AWS cloud, including ability to scale up and replicate test environments as needed, quicker development processes, and scalable and on-demand computing. Because of these benefits, EDF 9 is constantly innovating and able to scale per customer demand.

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Marcelo Schnettler, Senior Director of Software Engineering
April 14, 2016
Accelerate Go-To-Market
Speed in a CI/CD Environment

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Moody’s Analytics: Who are we?
Moody’s Analytics offers unique tools and best
practices for measuring and managing risk through
expertise and experience in credit analysis,
economic research and financial risk management.
By providing leading-edge software, advisory services, and research, including the
proprietary analysis of Moody’s Investors Service, Moody’s Analytics integrates
and customizes its offerings to address specific business challenges.
The Content Division is responsible for delivery of data and research through;
web services/APIs, as direct data loads and multiple websites, including
Moodys.com.

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EDF 9: In the Cloud
EDF 9
The Expected Default Frequency algorithm is a
proprietary model that measures the probability
that a firm will default over a specified period of
time (typically one year). EDF 9 is the 9th
version of this model and is currently running
on AWS.

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EDF 9: Before the Cloud
1. Models would be developed by analysts and economists using tools like Matlab or R
2. Many of years’ worth of data would be fed to the model for testing on servers in a data center
3. The models would churn through test data for a few weeks to produce an output
4. Repeat until the model is ready to be commercialized, then perform a thorough QA
5. If the model passed then convert to C++ and the re-QA
6. If the model passed then converted to C# and re-re-QA
7. Then integrated into existing applications and QA one last time before going to market
Net Effect:
• Four rounds of development
• Four rounds of QA
• Total time to complete: 3 - 4 years on average

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EDF 9: After the Cloud
1. Models are developed by analysts and economists using R
2. Many of years’ worth of data is fed to the model for testing on servers in the cloud, enough servers
would be spun up to finish a test run overnight
3. Repeat until the model is ready to be commercialized, then perform a thorough QA
4. Integrate the R directly into our application in the cloud
Net Effect:
• Two rounds of development
• Two rounds of QA
• Total time to complete: 1 year actual

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EDF 9: How did we Accelerate Time to Market?
• Initial research is greatly accelerated with the ability to scale up test environments to the point where
we can run an entire test cycle overnight instead of in weeks.
• Because the cloud allows us to use R directly, we can cut the development process in half.
• In the cloud we are not hampered by the tool sets that are supported at the datacenter, we can
employ the right tool for the job as needed.
• Rather than having to build a Dev, QA, Stg, and Prod environments, we build one environment that
evolves as the project evolves and as we need additional environments they the original
environment is used as a template to create new ones.
• Once in production we can scale up and scale down to accommodate the initial calculations to prime
the algorithm before going live.

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EDF 9: Additional Benefits of the Cloud
Constant innovation with regular new features:
• We’ve developed 3 additional variations on the original model in the last 9 months, each of which
would have to follow the old model in the pre-cloud world.
Scalability, quickly accommodate small to large workloads:
• In the data center model, it took us 17 days to re-calculate 40 years’ worth of data, in the cloud it
takes less than 17 minutes.
• If we wanted to we could make this under 17 second, but have not yet had need to do so.
• In the datacenter, it took about 20 hours to create the daily output of the algorithm and we only ran
the calculations once, now it takes about an hour to run and we run it as many times as is needed.
Accelerated go-to-market speed:
• It used to take 3-4 years to create a new model.
• Our fist time in the cloud it took us 1 year.
• In the future we expect it to be faster due to all that we’ve learned along the way.

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Thank you!
Marcelo Schnettler, Senior Director of Software Engineering