And last but not least, AI and machine learning, it now plays an important role in the underpinnings of everything that we do in operations and client services. For example, we do a lot of process analytics. We do load balancing. So, when a client calls, which agent or which group of people do we direct that client call to so that they can actually service the client most effectively. In the space of payments, we do a lot with machine learning. Fraud detection is another, and I will say that I’m so glad we’ve had the time to invest and think through all of these foundational capabilities. So, we are now poised and ready to take on the next big leap of changes that are right now at our fingertips, especially in the evolving world of AI and machine learning and of course the public cloud.

Laurel: Excellent. Yeah, you’ve certainly outlined the diversity of the firm’s offerings. So, when building new technologies and platforms, what are some of the working methodologies and practices that you employ to build at scale and then optimize those workflows?

So, focusing on outcome number one. Second, if you are given a problem, try and look at it from a bigger picture to see whether you can solve the pattern instead of that specific problem. So, I’ll give you an example. We built a chatbot called Casey. It’s one of the most loved products in our private bank right now. And Casey doesn’t do anything really complex, but what it does is solves a very common pattern, which is ask a few simple questions, get the inputs, join this with data services and join this with execution services and complete the task. And we have hundreds of thousands of tasks that Casey performs every single day. And one of them, especially a very simple functionality, the client wants a bank reference letter. Casey is called upon to do that thousands of times a month. And what used to take three or four hours to produce now takes like a few seconds.

So, it suddenly changes the outcome, changes productivity, and changes the happiness of people who are doing things that you know they themselves felt was mundane. So, solving the pattern, again, important. And last but not least, focusing on data is the other thing that’s helped us. Nothing can be improved if you don’t measure it. So, to give you an example of processes, the first thing we did was pick the most complex processes and mapped them out. We understood each step in the process, we understood the purpose of each step in the process, the time taken in each step, we started to question, do you really need this approval from this person? We observed that for the past six months, not one single thing has been rejected. So, is that even a meaningful approval to begin with?

Questioning if that process could be enhanced with AI, could AI automatically say, “Yes, please approve,” or “There’s a risk in this do not approve,” or “It’s okay, it needs a human review.” And then making those changes in our systems and flows and then obsessively measuring the impact of those changes. All of these have given us a lot of benefits. And I would say we’ve made significant progress just with these three principles of focus on outcome, focus on solving the pattern and focus on data and measurements in areas like client onboarding, in areas like maintaining client data, et cetera. So, this has been very helpful for us because in a bank like ours, scale is super important.

Laurel: Yeah, that’s a really great explanation. So, when new challenges do come along, like moving to the public cloud, how do you balance the opportunities of that scale, but also computing power and resources within the cost of the actual investment? How do you ensure that the shifts to the cloud are actually both financially and operationally efficient?

Vrinda: Great question. So obviously every technologist in the world is super excited with the advent of the public cloud. It gives us the powers of agility, economies of scale. We at JPMorgan Chase are able to leverage world class evolving capabilities at our fingertips. We have the ability also to partner with talented technologies at the cloud providers and many service providers that we work with that have advanced solutions that are available first on the public cloud. We are eager to get our hands on those. But with that comes a lot of responsibility because as a bank, we have to worry about security, client data, privacy, resilience, how are we going to operate in a multi-cloud environment because some data has to remain on-prem in our private cloud. So, there’s a lot of complexity, and we have engineers across the board who think a lot about this, and their day and night jobs are to try and figure this out.

As we think about moving to the public cloud in my area, I personally spend time thinking in depth about how we could build architectures that are financially efficient. And the reason I bring that up is because traditionally as we think about data centers where our hardware and software has been hosted, developers and architects haven’t had to worry about costs because you start with sizing the infrastructure, you order that infrastructure, it’s captive, it remains in the data center, and you can expand it, but it’s a one-time cost each time that you upgrade. With the cloud, that situation changes dramatically. It’s both an opportunity but also a risk. So, a financial lens then becomes super important right at the outset. Let me give you a couple of examples of what I mean. Developers in the public cloud have a lot of power, and with that power comes responsibility.