DTCC Brings Hypothetical Market Scenarios with Snowflake Native Apps

DTCC stands at the center of global trading activity, processing trillions of dollars of securities transactions on a daily basis. This year, DTCC celebrates its 50th anniversary, and over the past five decades we’ve led the financial services industry through unprecedented historical events and market disruptions. The market volatility we’ve seen in recent years has only underscored the value of using data to model and analyze market behavior and avoid to manage risk for the Street. 

As part of our commitment to the industry, we run quantitative “what if” models and analyses on client portfolios to accurately project the associated risk to our clearing services. Throughout the years, we’ve built capabilities to analyze not only actual portfolios, but purely hypothetical portfolios that our analysts and clients leverage to model theoretical scenarios.

To make all of this happen, our developers and engineers needed to:

  • Make the model interfaces flexible for both real and hypothetical data
  • Provide the compute power to run the models on the fly
  • Present the results quickly and efficiently to our machine and human users

Managing and scaling a platform that does all of this is not a trivial task. DTCC has done this successfully to date, and our goal is to provide even more of these value-added services through the Snowflake Native App Framework (currently in private preview).

Expanding the DTCC ecosystem with Snowflake Native Apps

To explain how DTCC is leveraging Snowflake Native Apps, I first need to paint the broader picture of the DTCC Data Cloud on Snowflake. 

Our philosophy is to take the output of our transactional systems and engineer that data in near real time onto the Data Cloud, where it automatically becomes part of a data ecosystem that has analytical depth and dimensional breadth. In the end, we want all of DTCC’s data securely accessible to our internal and external stakeholders. 

Given our mature data landscape, the attractiveness of Snowflake Native Apps is that they allow us to leverage compute capabilities directly where the data already lives. The way we’d normally design analytical applications is to build something outside of our data environment, manage the infrastructure and compute, connect to Snowflake for the data, take the data back to the compute platform, and give an interface to our users. This presents what I like to call an “efficiency opportunity.” 

Snowflake Native Apps allow my team to manage the application layer in much the same way that we manage our Data Cloud layer. My team and their data skillsets can be utilized beyond just engineering and warehousing. Streamlit specifically allows our engineers to create a rich UX without having to master a full front-end stack. If Streamlit becomes that ultimate layer where a Snowflake engineer can develop an application soup-to-nuts on the platform—that’s a great outcome.

Broadening the client experience with the Data Cloud 

Back to our hypothetical portfolio use case, we are now able to bring our models and UX to our existing data platform through a Snowflake Native App. Moreover, by using Snowflake’s Secure Data Sharing and “share back” capabilities, we can bring this capability to our clients’ data platforms as well. This allows for portfolio adjustments to be communicated either through data sharing, or through the Streamlit front-end where the data can be quickly run through our models. When you bring this all together, it’s a fully managed solution that provides value for our internal and external stakeholders.

What’s been key in our journey with Snowflake is that we built a solid data layer first. With that environment on Snowflake, we are able to leverage Snowflake Native Apps and Streamlit without having to invest in and manage other resources.

Snowflake and DTCC: Bringing the future into focus

Our vision moving forward is to continue building out the DTCC Data Cloud on Snowflake. With Snowflake Native Apps, we intend to give our clients a seamless experience between their day-to-day apps and hypothetical scenario modeling. By making Snowflake Native Apps and data sharing part of our service offerings, we can put the power of DTCC’s data and applications directly in the hands of our clients, so they can realize the benefits of the many years of innovation we’ve put into this dynamic platform.

Hear more about DTCC’s experiences with Snowflake Native Apps during their session at Snowflake Summit 2023. Click here for all the details.

Forward-Looking Statements

This blog contains express and implied forward-looking statements, including statements regarding Snowflake and DTCC’s products, services, and technology offerings that are under development. These forward-looking statements are subject to a number of risks, uncertainties and assumptions. In light of these risks, uncertainties, and assumptions, actual results could differ materially and adversely from those anticipated or implied in the forward-looking statements. As a result, you should not rely on any forward-looking statements as predictions of future events.

Source

Leave a Reply