Sarva Srinivasan joins host Brian Thomas on The Digital Executive Podcast.

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Welcome to Coruzant Technologies, home of the Digital Executive Podcast.

[00:00:12] Brian Thomas: Welcome to the Digital Executive. Today’s guest is Sarva Srinivasan. Sarva Srinivasan is the Founder and CEO of EZOPS, an AI focused fintech firm that offers a cloud-based data management and productivity enhancement platform. Sarva has experience in global sales, product engineering, and deployment of targeted technology and operations solutions to financial institutions across the globe.

Before founding EZOPS, he worked at firms including Calypso, Oracle Financial Services, and two startups spanning 30 years.

Well, good afternoon, Sarva. Welcome to the show.

[00:00:48] Sarva Srinivasan: Thanks for having me, Brian. Great to be with you.

[00:00:51] Brian Thomas: Absolutely. This is awesome. Again, first podcast of the day, so we’re getting into it. I know you’re based out of the out in the New York, New Jersey area, and I appreciate you making the time for us today.

So, jumping in, Sarva, you’ve got quite the career in technology, in sales, you were a senior executive, an entrepreneur, and now the founder of EZOPS. Could you share with our audience the secret to your career growth and what inspires you?

[00:01:16] Sarva Srinivasan: Sure, sure, Brian. When I think of my career growth, there have been a number of factors that contributed to the growth, but what’s been a constant is the people, right?

And it starts from the beginning, dad and mom, and they came from very humble beginnings. And despite that dad studied to be an engineer and mom was a middle school principal, and that’s important because they had to really put themselves through a lot of stress to get to where they got to. And very early on, they instilled in me the need for hard work.

And these values have kept me grounded all along. Along the way, I’ve had the opportunity to work with a number of truly wonderful mentors, talented colleagues and demanding supportive clients. And the last part is important because you want your clients to be demanding, but supportive at the same time, because that allows you to learn from your mistakes.

And each of them helped shape the way I think and operate. It also helped that very early on. In my career, I realized the joy of working with startups and early-stage companies. And that sort of helps me as I continue this journey with EZOPS. But last, but not the least, you need really good partners, both in life and work to be a successful entrepreneur.

And I’m truly blessed to have both at this time. And the second part you asked for is what inspires me. I think it’s an opportunity to pay it forward. Because when we look back at our lives we remember folks that helped us along the way without really expecting anything in return. I truly believe that if I can help 20 people and they can help 20 more, the circle of influence would grow.

Those that help me inspire me to help others to succeed. And given that a large part of my current sphere of influence is my team and my clients, I wake up most mornings thinking about how I can help them succeed both professionally and personally.

[00:02:53] Brian Thomas: That’s awesome. Thank you. And you brought it back to what I say is center right back to the people, you talked about your parents and having other people in your life being great influence in your life. And of course, at the end of the day we’re truly blessed by giving back. We’ve received a lot through our life and now we give back. And that’s something that we really talk about here on the podcast. So, thank you. Appreciate that. Sarva, let’s jump into your platform here.

You help some of the world’s largest financial service institutions with your platform. You harness the power of machine learning and intelligent process automation. To revolutionize data control and drive transformation efficiency gains. Can you walk us through this process at a high level?

[00:03:36] Sarva Srinivasan: Sure. So, before I really talk about the platform and the process I would like to paint a picture of why what we do is critical.

If you look at the last 20 plus years, there’s been a confluence of, a number of outlier events. And advances in technology. And so, we’ve had the dot com bubble, the great recession of 2008 and most recently the pandemic. And along the way, technology has been continuously evolving, be it cloud computing, AI or blockchain.

And each of these have contributed to the volume and velocity of data and data has been growing exponentially. And some of this is due to the regulations since 2008, the digital transformations that got accelerated with the pandemic or the automation that was needed to drive efficiencies. Now, as clients or enterprises go through what I call the data journey, lack of quality data can have dramatic impact on the expected outcomes.

And quality of data is what we address at EZOPS. Imagine not having the right insight into your savings or if you’re a hedge fund the amount of cash available for trading, or if you’re a small business, just your payables and receivables, right? Some of them can go out of business. You don’t have this data at the right time.

So now, stepping into what we do at EZOPS, we typically break the data journey into four pillars. The first pillar is what I call data acquisition. This is the ability to acquire vast quantities of data from a plethora of data providers and sources, and this could be your brokers, your custodians, your mortgage application systems, retail banking and systems credit card applications so forth.

Right? So there’s a lot of data that gets used in banking and financial services, both structured and unstructured data. The 2nd part is once you get the data, what do you do with it? You have to go through a process of validating the data and ensuring that it has got the right quality. Up until maybe a couple of years ago, rules-based approaches where the approach that was possible.

But in the last 3, 4 years, we’ve seen air being used very heavily and use both. We use both the rules based on a based approach to detect these defects and anomalies. And once you know, these defects and anomalies, you want to be able to swiftly. Fix them. And that’s where the automation piece comes in.

So we automate some of the processes that our clients use to address these anomalies. And lastly, now you have a lot of data that has been produced by the application as well as by bringing in data from the external world. And you want to get some insights out of that. So those are the 4 pillars that we leverage and help our clients generate what I call operational alpha and you have the trading alpha. And similarly, operational efficiency, reduce risk and cost, gets in the operational alpha that they’re looking for.

[00:06:01] Brian Thomas: I appreciate you breaking that down for us as well. There’s been a lot of obviously again, in the fintech finance world, a lot of regulations that have come out in the last several years but having to navigate all these different changes within our environment.

And we have access to all this petabytes, zettabyte of information, right? So we have to use some automated processes using AI to get this done. So I appreciate the share on that today and Sarva it with the recent proliferation of AI platforms, such as ChatGPT, BARD, and other gen AI deep learning platforms, how do you plan to stay ahead of your competition?

[00:06:38] Sarva Srinivasan: I think of technology as an enabler of the business solution we bring to our clients. So we think of the advances that have been happening as additive to what we do especially banking and financial services, deals with a lot of unstructured data and reports in written form.

And you’ll be surprised even now, there are situations where people still use faxes and scan copies of documents. By leveraging LLMs in the form of, say, ChatGPT or Bard or equivalent applications that are there that we provide insights from these data sets. So, we’re incorporating some of these capabilities as part of our customer support process so we can identify patterns and proactively address issues.

That’s not easily visible to the human eye. Our ability to look around the corners is not there when we use the rule based, when we use a rule-based approach, but that’s been now enabled thanks to the availability of computing power as well as these AI models. And we’ve had anomaly detection models deployed for a while now to basically monitor these defects and deviations. Again with the idea being that the clients can address these quickly if they get to know of them sooner than later.

So, our plan is to combine our domain expertise, which is a key differentiator for us. It’s not just the technology, but it’s also the domain that is important. So, we combine our domain expertise, along with platforms like ChatGPT. Now we’re looking at generative AI to see how we can actually improve our customer experience. Accelerate their ability to reduce risk and cost

[00:08:04] Brian Thomas: Thank you. And I appreciate that. We’ve got a lot of technologists in our audience and love to hear more and understand what people are doing out there as far as your platform. And I know you can’t get into all the details of your platform, but we do appreciate the sharing on that.

And Sarva last question, you’re obviously leveraging some of that new and emerging tech in your tech stack. Is there something you want to highlight for us in your tech stack that would get us all juiced up around emerging tech today?

[00:08:29] Sarva Srinivasan: Sure. Sure. And I’ll try and stay within the confines of what I can share publicly.

Given that we’re working with operations and business teams that are clients and these clients are largely banking and financial institutions, could be banks, regional banks, asset managers hedge funds and so on focus has been to produce a platform that encompasses the 4 pillars that I talked about the data acquisition, data validation, automation and insights. While also making itself service, this is important because at the end of it, we are trying to generate operational alpha. And if you enable somebody to do their work easily, oftentimes, the results and gaining efficiencies. So, we use self-learning algorithm to assist our users as they take operational decisions.

Most of which are repeat actions, right? So when you look at operations tasks, they’re mostly repetitive. And the idea is, help relieve them of the drudgery of these repetitive tasks. We also augment the data that is available to them, as in, the exceptions, the defects, the anomalies with additional information that we get from data feeds and commentary.

And this could be private commentary or public commentary really depends on the access to. Information that we have. Now with the advances that are happening in technology and computing where data is sort of coming together easily. And we leverage snowflake. We leverage AWS. So, with data being centralized, it’s a lot easier to access the data and make sense of it and then make that available to the users.

So, when they really start the day. Not only do they know what the challenges are, what the issues are, but they also have additional information that allows them to act on it swiftly. We leverage a lot of open-source capabilities. Be it for identity management or anomaly detection or process automation while we continue to build a significant amount of a product ground up.

And the reason is, when you build a ground up, we can continue to innovate. And even though open source has a certain set of challenges, it definitely helps us accelerate our go to market. So, that’s something that we do quite a bit. At this point, we are experimenting with generative AI.

And I say experimenting, because it is not in production net to see. If we can help forecast resource requirements and seasonality at our clients. And this is important because, in a lot of times, like I said, there are repetitive tasks, there are weekly processes that run the monthly processes that happen all the time.

It’s very difficult to plan for the resourcing capacity, both infrastructure and human during these phases, or there is an event in the market. The markets are sort of exploding and there’s a lot of work that lands up with some of the middle office and back-office operations teams that banks have.

So, what we are working on is to see, look at past patterns and then project out as to where that is. Those are going to be because I think just an infrastructure can have significant savings when you do something like that. As we look at some of these landscapes, and the changes that are happening, especially when it comes to emerging technology, I think cloud computing, something that we use quite heavily AI is embedded into the product.

Blockchain, we considered using as part of some of our offerings, but then we decided to stick with AI because just operations, I think is better enabled with AI. And there’s a lot of talk about quantum computing and people are looking at what we can do with that. There’s always some experimentation going on within the organization around these areas.

And I think they are here to stay and hold the key to our future. And I’m just glad to be part of these exciting times.

[00:11:36] Brian Thomas: That’s awesome. Thank you again for unpacking all that. I do appreciate it. I know sometimes there’s things that you can’t share with the audience due to some confidentiality or some sort of NDA that needs to be in place before you share some of this stuff, especially publicly.

So, thank you. And Sarva also, you wanted to mention. If you could just very briefly touch on the, the experience of entrepreneurship. We’ve got a lot of entrepreneurs in our audience. If you want to touch on that real quick, I know you would like to mention something on that. I’d love to hear about it.

[00:12:04] Sarva Srinivasan: Well, thanks, Brian. I think that’s a topic that’s very close to my heart. And going back to what I said in terms of those that helped me get where to get where I am, right? mean, There are a few things that they advise me on, and I think it has helped me along the way. One is, Understand your customer, right?

So you truly put yourself in their shoes, understand why they’re doing what they’re doing, how they are using what you’re producing, what is working for them and what’s not working for them. The second part is Team. Going back to my focus around people build a team that complements you. As tech is evolving very fast, and it’s a vast area, it’s very difficult to do all of this by just having 1 or 2 folks, which you really want to have a team that can compliment you as the tech is evolving. Lastly, this is important advice somebody gave me. If you have to fail, fail fast and learn from the failure.

[00:12:51] Brian Thomas: Love that. Thank you for sharing those three gems. Again, we appreciate it so much. There’s so much invaluable information on these podcasts, and I’m so happy to be able to share your story out with our global audience. Sarva, I really do appreciate it, and it was a pleasure having you on today and I look forward to speaking with you real soon.

[00:13:11] Sarva Srinivasan: Thanks, Brian. Have a wonderful day ahead.

[00:13:13] Brian Thomas: Bye for now.

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