Sagent CTO Uday Devalla on how to transform servicing

And how AI reminds him of the rush to cloud computing

Editor in Chief Sarah Wheeler sat down with Uday Devalla, chief technology officer at Sagent and a 2023 Vanguard, to talk about his focus on transforming servicing, what really counts with data and the right approach to using artificial intelligence.

Sarah Wheeler: After spending several decades leading tech at lenders, including Stearns and Bank of America, what drew you to join Sagent four years ago?

Uday Devalla: What attracted me here is primarily an opportunity to transform the servicing industry because while we’ve seen a lot of transformation on the origination side, nothing has really changed on the servicing side. And at Sagent, we have an opportunity to do that. So we’re focused on this new platform that is making the whole experience more borrower-centric with automation and AI.

SW: Where do you start to transform servicing?

UD: We start with data because ultimately, data is the foundation for everything else. And one of the biggest drivers for what we’re doing is end-to-end data. This is what we’re focusing our strategy on, and in the servicing industry this really doesn’t exist.

SW: Data has been a hot topic for years, but the conversation has really changed in the last several years. What do you see there?

UD: Everybody has some data project going on, right? Whether you’re a service or a software provider. And a lot of focus is on trying to bring all the data together and our customers and the whole industry has had to spend an incredible amount of time and capital on building these large data sources, because the source data is in multiple locations.

From a transformation perspective, there’s still a lot of challenges. If your sources are not good, then even if you try to build a large data warehouse or large platform, you will have a lot of challenges with data. And that’s what we are addressing: we are ensuring that the source data, from an end-to-end servicing perspective, is immediately available for our customers. And that’s a big difference in how we are looking at data and how everybody else is looking at it.

SW: How is Sagent leveraging AI?

UD: We’ve been using AI for a while now in terms of predictive modeling and we have a really sophisticated platform for document classification and extraction of data and so on, that is performing very, very well at scale. More recently, we’re incorporating generative AI into our capabilities, adding that to the platform — from assisting the operators in terms of how they can do agent-assisted chatting, predictive decision-making for call center agents — where it makes sense.

Generative AI is obviously a huge milestone in terms of the development of overall AI. But from a use-case perspective, we want to use that for two things. One is definitely to make the consumer experience better in terms of handling some service requests, and guiding the consumer to whatever is needed on their site. And then two is to increase the efficiency of the operators by using generative AI to provide them the right assets at the right time. So they can go faster and bring down call resolution times.

SW: What else would you say about the possibilities of AI?

UD: Personally, I think a lot of people are jumping into AI without being prepared for it. It reminds me of the early days of cloud computing, and how everybody jumped on it. But then all they did was ship their data centers to the cloud, and that was very, very expensive. People realized that to actually use their own computing, you have to transform your platform architecture. And that journey took years for people — in places maybe 10 years.

There are a lot of similarities here where people are jumping into AI without having the foundational elements of AI, which is data. And when I say data, there’s a lot of data out in the world today, even from a business perspective, it’s just massive. But having a lot of data means nothing if you don’t have good data hygiene, good data pipelines and the right governance. And that’s what is lacking in many organizations. So when they jump into AI, they could spend a lot of money and not necessarily get the return on it. For our customers, we are very focused on solving that problem.

SW: How do you think about cybersecurity?

UD: We take cybersecurity very seriously, obviously, and we did even before these big cybersecurity incidents that have happened in the last three months. We have always been pretty focused on cyber security because we host others’ data, right? I mean, we have 42 customers, and there’s more implementations going on right now so it will shortly be 45.

And being a technology company, our approach is different than a lenders’ approach, for instance. We don’t have hundreds of thousands of people in many different geographies. So our footprint is smaller and the vector is smaller in terms of security. Even then, in terms of the controls that we put in place, our security framework is very rigorous. Our No. 1 goal is protecting customer data and avoiding downtime. But these days, it’s impossible to say that you’re fully secure — if the Department of Defense can’t protect their systems, no one is completely secure. So we are also very focused on protecting ourselves from incidents that happen in the broader ecosystem, whether that’s somebody big like Microsoft or someone smaller.

SW: What is something cool about Sagent’s technology that people might not know?

UD: The scale and efficiency at which we operate. We support more than 40 servicers in the industry and our platform handles billions in payments on a monthly basis. And as we are focused on the transformation of the industry, I think a lot of people don’t realize that the tech that we are building into the platform is very, very sophisticated. It’s highly scalable, in terms of making open APIs to our customers, and there’s a lot of modern tech that we are enabling to our customers.

SW: How does the company culture support tech innovation?

UD: We think of ourselves as a fintech, so we have smaller teams and decision-making is not necessarily centralized. We empower our teams to focus on consumers and address the needs of our customers’ consumers first before we do anything else — and that is pervasive across the whole organization. When a customer asks something, everybody is like, ok, we have to address that immediately. And that’s not just in the customer success organization, it’s in dev ops and engineering.

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