Federal Reserve Bank of Boston Innovation & Technology Culture

What’s your rule for fast, safe releases — and what KPI proves it works?

Immutability and idempotency. Since our platform supports instant payments for financial institutions, while we do value speed, the safety of our releases is our top priority. To keep our releases as fast, safe and predictable as possible, we package and lock in as much of our application and infrastructure code as possible and do not allow changes as we move through delivery stages. Idempotency comes into play for our infrastructure. With everything managed through code, we can detect upcoming changes before they’re made, and only touch resources that require a change. Our release success rate is a testament to this working. The more we put in code and the less we allow for differences between delivery stages, the more our releases have completed successfully.

Which standard or metric defines “quality” in your stack?

When we were first starting, our process was fairly manual. We made sure to package and automate wherever possible, but there were some growing pains. Our delivery stages were not always consistent. From a platform perspective, I would define quality with consistency by means of abstraction for the teams through infrastructure and automation code standards. By providing paved roads — pre-architected, automated paths — we ensure the “right way” is also the “easy way.” This bakes quality and security standards directly into our workflows, allowing teams to ship in a consistent, reliable manner without the overhead of manual steps. Abstraction allows our teams to focus on their priorities and not need to worry about what happens behind the scenes; it just works for them.

Name one recent AI or automation deployment and its impact on the team or business.

It’s honestly hard to pick just one because our team has intentionally built a culture where automation is a constant, collective effort. We protect that through maintaining a continuous feedback loop with our teams. We don’t just build what we think they need; we actively gather pain points to ensure we’re solving the right problems. For example, engineers made it known that our code review process was inefficient, causing frustration. We listened, then designed and built a service to automate identifying and notifying the right reviewers at the right time, turning a friction point into a streamlined workflow. On the business side, we built an automation engine to handle our unique requirements, and now it’s the backbone of every single deployment, allowing us to consistently deliver faster and safer.

What’s it like to work on the data and analytics team at the Federal Reserve Bank of Boston?

I’ve spent 13 years at the Federal Reserve on multiple teams. The organization is constantly changing, but the Fed is consistent about its investment in people and the passion our teams have for the Federal Reserve’s mission to promote economic growth and stability in New England and the nation.

As part of the FedNow Service’s product fraud mitigation team, I work primarily on customer-facing products. We help tackle real-life problems for financial institutions. It’s exciting to design solutions for our customers, and it’s even more satisfying to see those products protect their downstream customers. Anything we provide that helps a financial institution stop a customer from getting scammed or defrauded is a win. 

What tools, systems or processes help your team deliver useful insights?

Knowing the latest technology and techniques is critical. It helps that data is quickly becoming less complex and more accessible. Instead of relying on complicated code and inflexible dashboards to find and interpret data, users can get what they need by asking in natural, conversational language. AI is fundamentally changing how we interact with data and deliver insights, and I know things will continue to evolve. We are lucky to be in an organization that’s so focused on training us how to use the latest tools to best reach our customers.

My team is constantly exploring different ways to support customers and colleagues with the data available to us. That includes using AI to make insights more actionable. But data work is never static. You must consistently iterate and respond to quick demands. We build in a way that lets us do that.

The Federal Reserve emphasizes continuous training and engagement. I’ve been on different data teams within the Federal Reserve, and I’ve never been denied training that I’ve requested. They really invest in people and in improving our skills.

How does your team partner with stakeholders to turn data into action?

We never build products in a vacuum. We set up systems that can work seamlessly with our customers’ systems and give useful insights. This usually isn’t just data analysis. Our team partners closely with data scientists, Agile Release Train teams, and, most importantly, our customers. We have ongoing conversations with stakeholders to better understand their needs, not just about the data they want, but about what products can help them integrate it quickly.

Internally, we work to break down organizational silos and partner with other teams. Our leadership is focused on data-driven decision-making. From pricing to operations and product design, our organization relies heavily on our data teams to help determine its direction. The combination of mission-driven work, continuous learning opportunities and collaborative problem-solving creates an environment where data teams can drive smarter decisions that protect real people from financial harm.