Behind the Partnership:
How GDS Link and Stratyfy Are Building Trust in Credit Decisioning
Bringing transparency, intelligence, and fairness to every lending decision
How is data science changing the way we lend? In this episode of The Lending Link, host Nathan George sits down with Aarthi Muthukrishnan, Chief Product Officer at GDS Link, and Jose Tagunicar, Head of Product Strategy at Stratyfy, to discuss how AI, transparency, and ongoing learning are shaping credit decisions.
They talk about what makes a good lending decision today, focusing on more than just reducing losses. Profitability, fairness, and trust are just as important. Aarthi explains how GDS Link’s new Decisioning Platform helps lenders act quickly and make smarter, more confident choices, while Jose shares how Stratyfy’s rules engine makes AI decisions clearer and more consistent.
Together, they dive into real-time bias detection, automated optimization, and how advanced analytics are now within reach for community and mid-sized lenders, showing what the next generation of smarter, more transparent credit decisioning really looks like.
Episode Transcript
Nathan George: Let's go ahead and get started. Welcome to the podcast. Excited to be chatting today with Jose and Aarthi about all kinds of data analytics, about the new GDS capabilities enabled by our partnership with Stratyfy, and how we can work together to help lenders. So it's really great to have everyone on Jose. I guess I'll get started with you. It was good to see you at the LEND360 show this week to meet you in person and enjoyed our conversation. You've had a lot of experience at bureaus in both sides of the Atlantic with all kinds of products like credit cards and now data analytics. Maybe you could start, give us a kind of a big, a quick background and what maybe a personal question too. With the work that you're doing now, what's the most meaningful part of your job that you're doing on a daily basis? You work long hours. Why are you doing it?
Jose Tagunicar: Well, thank you, Nathan. I appreciate it. So it was very nice meeting you as well. Quick, fun fact before we dive in, I don't know how to ride a bike. Okay. I figured I'd start with that because since everything I do in life is about balance. So I started my career helping lenders make better credit decisions. I spent 13 years in credit cards, 10 years at analytics companies, and a few years at the bureaus. And I've worked in projects across six continents. And I actually thought I'd be a teacher, which is why I love that light bulb moment when something complex suddenly makes sense. At Stratyfy, I get to do that every day. Help lenders of all sizes make faster, fairer, better decisions. Through our partnership with GDS, we're helping banks see that AI is no longer a future promise and that it's here. So I enjoy. That's what I enjoy doing every day. I've been a banker all my career and worked in analytics. So it's great to be here at Stratyfy and working with folks at GDS.
Nathan George: That's a great way of putting it. I like the bike analogy and the balance. It reminds me of how my grandfather taught me how to ride a bike. He put me on and just pushed me down a hill. So get enough speed going and momentum and things work out. That's like the Silicon Valley model, right? Aarthi, let's turn to you for a minute. I would like for you to give an intro, but I also know two things. Well, a couple of things about you. One is you really like doing the maker events and things like that, so maybe you could talk a little bit about that as well as a little bit about what you do at GDS.
Aarthi Muthukrishnan: Sure. So yeah, I lead a little bit of a double life and like Jose, I found out later in my life that I love to teach and that light bulb moment is definitely very satisfying and you know, makes, makes you. Makes it, makes it very, makes it very gratifying to you and you know, as a person. So yes, my, as a side gig, I also run a makerspace where I focus a lot on, you know, engineering and electronics and bridging the gap between art and science and tech with little kids all the way from elementary to middle schoolers and even adults. So that's my, my side job. That kind of feeds my soul not to say that what I do and do at GDS doesn't. It's actually been such a wonderful journey the last 18 months and I'm defining what product strategy looks like for GDS and where we are headed. My past career, my last 20 plus years has been in data and analytics in and around risk and marketing for large national banks in the US and Europe primarily. And I worked for Verus Financial Services very much as a consultant for a large part of my career where I would develop models or lead teams that were developing risk strategies, but we were never in the workflow of implementing those strategies. Right. So at GDS, it feels full circle for me where now I'm at the other end where I'm taking seeing the implementation of the risk strategies, the models that our lenders and our customers have developed and how do we then execute it with a lot of precision to make sure that they're successful. So that's been really amazing. But then I also see the potential of what we can do better because of my background in data and analytics and how we can take it further there. And that's why some of the capabilities that we are bringing to light with Stratyfy, it really excite me. And I'm so looking forward to what we're about to unleash here together.
Jose Tagunicar: Isn't it interesting? Aarthi, we both come from, our background is from large organizations and you know, as I kind of worked across the globe and here in the US there's this like 9,000 other smaller banks that have been left behind. And so part of the, part of our challenge and which I love to do is, you know, everything that we learned in large banks, how do we push it down and create a level playing field for our customers and also their clients.
Aarthi Muthukrishnan: So yeah, it's a whole, it's a huge, very long tail that when you work only in the big national banks, you kind of lose sight of that. There's actually a huge spectrum of lenders and different variety of lenders that may not necessarily get the mind share and they're smaller teams with one or two people and they don't have the resources of a big bank. And to be able to help them and through them, help consumers who are looking for these products so that they can impact their lives makes a lot of sense to me. Right.
Nathan George: We're going to talk a little bit about expanding that access a little later when we get into the why do we partner? Before we get too much deeper into that though, I wanted to ask both of you a question about credit decisions in general. There's been a lot of change, although when you look underneath the covers, it seems like maybe there's not so much change, just where, you know, things have definitely sped up. But at the end of the day, you're still trying to evaluate risk. Right. And there's different ways to do it. How would you say, you know, a good decision, credit decision strategy, how would you say that's evolved over the last five or 10 years? We've got this, you know, we're barreling towards the singularity with AI tools and things like that. And Skynet will be here soon for us. But how has access to those tools and things, has that changed credit decisions? And what does a good approach look like today? And how's it different from maybe what we had five or 10 years ago?
Aarthi Muthukrishnan: I mean, my experience has been like, we've always had the credit bureaus at the center of most credit strategies. Right. Going back 20 years ago, that was always the case. Almost everybody uses some kind of risk score, FICO score cutoff. What has changed, I think over the last decade or so is the access of alternate data, additional data to supplement the gaps that you may have with the FICO or wherever you have a thin file or not enough information or get richer data and be able to differentiate further into the segment. So there's the difference in the amount of data that's available. And then the second thing that I would say is in the methodologies that we now have access to. Right. So once, say, 15 years ago, big data was the buzzword, not so anymore. Right. And so you went from, okay, being able to process large data sets that enabled us to do machine learning and random forests and XGBoost. All of those techniques became normalized and the larger banks with more resources kind of went on the journey. And along with that journey, the regulators came on the journey as well. So we have done a lot of work in terms of both the sophistication of the methodology as well as the data underneath it in the last several years. And now we're seeing the next leap forward with some of the Gen AI methods but yet to see how, where that leads us because it is different, categorically different from some of the previous generation of technologies that have come our way.
Jose Tagunicar: Yeah, no, definitely. I mean the availability of data is one of the most transformative things that has been happening. But I think I want to kind of pivot a little bit Nathan here in that early in my career a good decision meant keeping the losses low. Over time I realized that a great decision is balancing risk with some sort of opportunity. So risk management I think over time has evolved from loss control to more of a profit management type of organization. After the financial shock of what over 10 years ago, you know, risk management has evolved from what I, I kind of call planning from A to Z, no longer planning from A to B to C. And you know, they're using tracking, measurement, forecasting, contingency, all these things are happening all at once. And you know, analytics has always been kind of like that forward looking, but now it's directly tied to a bank's KPI with compliance and agility as well. And so with so many tools available, so much data, the question isn't what works now, really it's, you know, to Aarthi's point, it's what fits the future. And platforms have to connect marketing, fraud, collections, risk, everything while still delivering safety, soundness and more importantly ROI. So this is kind of like what excites me about GDS and Stratyfy and that GDS brings that trusted infrastructure for decisions and tracking and compliance and all that. And Stratyfy, you know, we add the analytics, the intelligence. I know we'll talk about optimization later and transparency later. So it's, it's almost like a good fit here. And you know, scores and models are great, but when they're used in real decisions, that's when it matters the most. And a good, a good lending decision today isn't about saying no safely, it's about saying yes intelligently. So that's kind of like how risk management and good lending has shifted, in
Nathan George: my opinion, optimizing for profit rather than just like you said, loss.
Aarthi Muthukrishnan: Yeah, organizations where risk has been very siloed. Right. And then you just are optimizing for losses. But that mindset definitely seems to have shifted over the last 10 years or so.
Jose Tagunicar: Yeah, it's completely shifted and you know, it's. I think Aarthi. I don't know, I mean back early 2000s, like the big banks were already thinking that way. Right. And now you just kind of see it coming down the ranks and I think it's great, but it presents a challenge.
Nathan George: It's interesting you say that. I heard a very interesting podcast from the Fintech Takes guys, which highly recommend that one. They were talking about the back and forth between a measure of a borrower's willingness to pay versus measuring their ability to pay and connecting that to also to fairness, frankly, and moving away from, you know, subjective decision making of the guy behind the desk at the local bank to something that's more objective and fits with the company's goals and compliance and regulation and profitability and things like that. It was a really interesting back and forth, you know, as you think about data and the AI capabilities of AI to have small companies leveraging that data. I saw in one of your releases, Jose, the mention of transparent transparency and governance as critical factors, competitive factors for you guys. Could you maybe talk a little bit about that, particularly with respect to AI? But why is that so important with lending today?
Jose Tagunicar: Yeah, so I think transparency is kind of like the currency of trust today. Regulators, auditors, customers, they all want to know why a decision was made. And black box AI doesn't work for lending. And so, you know, and a lot of the smaller lenders and mid sized lenders, they use rule based systems. But rules based systems, they're transparent but they kind of sometimes fall short on accuracy. And advanced AI are accurate but they're hard to explain. And so with Stratyfy's patent pending technology called Probabilistic Rules Engine, or we call PRE, we kind of bring those worlds together. The interpretability part and the predictive power all kind of in one. And so transparency for us isn't about, isn't just about compliance. It's about confidence. And when you can explain every decision that you make, you actually move faster, you test more and you're very confident about the outcomes that you have. And so, you know, I think when we hear about all of these different methodologies and all of these different techniques, you have to choose the one that really gains the confidence of your customers.
Aarthi Muthukrishnan: I think that confidence comes from the aspect of being able to get repeatability. So you get the same answer no matter how many times you ask the same question. There are aspects of Gen AI today that don't get you there and then you lose confidence and trust in, in those systems. So to have something that is, you're confident that it's always going to, is going to work as planned, like these are the rules that you set up. This is the structure is super important in being able to explain the rationale behind the decisions to your customers and to your regulators.
Jose Tagunicar: Right.
Nathan George: It seems like there's two sides to that trust coin. Right. The internal trust side that you're getting objective. Right. Reliable data you can make business decisions on. But then you've got the outside the circle of your organization with public, whether that's regulators or with your customers. Right. You've got to have trust on both sides. I'm wondering though, you know, implementing things like that can be a real challenge maybe for smaller organizations or even large organizations. Oftentimes they've got a lot of projects in the works at any one time and getting resources on them is tough. You know, Aarthi, maybe you could take this one or start with this one. What are some of the ways you're empowering these institutions of different sizes to access this kind of tool, this transparency, build this trust and be able to take advantage of the capabilities that we've been talking about.
Aarthi Muthukrishnan: I mean the core of our product is really helping our customers implement their policies with a lot of speed and precision. That's primarily what we do very well at GDS. We do this through our connectors. We are connected to almost every data source you can think of. We are never down, we're very reliable. So we are able to make it very easy for customers to connect to these data. So take out the friction from getting the data in through the door. That sometimes is the hardest thing to do. Take the data in, get it in a format that can be easily executed upon and make a decision in real time. We are making decisions in real time for so many of our customers across the globe that I think is super powerful. Especially for lenders who are just getting off the ground or even lenders who are, who may have a large team who are well settled but don't want to take that infrastructure in house, don't want to be responsible for maintaining a large system that is connecting and keeping track of all the credentials for the different data sources and all of that. And we take that, take that out of that, take that friction out of our customers hands and help them with that.
Nathan George: Are there examples? I mean, most of what we've been talking about has been primarily on credit decision origination decisions. Are there other areas where this, this type of capability being able to monitor and optimize using new data in an automated fashion, those kinds of things. Are there other examples outside of credit decisions where this could be helpful?
Jose Tagunicar: Yeah, I mean, I mean, from Stratyfy's point of view, you know, while optimization is at the heart of what we do, any decision you make across the life cycle can be optimized. With what Aarthi was mentioning about the plethora of data out there, the use cases for account management, for collections, for segmentation, it's going to rely on a lot of data and the real time decision that Aarthi was mentioning and what GDS is great at, you know, it's all about competition and getting the right product in the right and the hands of the customer at the right time. And so I don't know if both of you have kids that are Gen Alpha or Gen Z, but their expectation is now. And you know, fortunately for all of us, I mean, real time decisions are table stakes. Right. And so when you're, when the big banks are doing it and you know, we could argue whether or not they're doing it well, but you know, it's everybody that has. Everyone has to move into real time decisioning with the right data because the customers are expecting personalization. Aarthi, what do you think?
Aarthi Muthukrishnan: I agree. I mean, that is definitely table stakes. Right. And what, what does that mean for a lender? It basically means that you have to have a credit strategy that is very clear in its outcomes. You're minimizing the number of manual reviews as much as possible, so you're able to get that decision to a customer as quickly as possible. And that may mean to get to that level of understanding of your customer's credit risk, health and the financial health. And you may need more data sooner, you may need more sophistication in parsing that data and understanding the shades of gray. And you then have to have a strategy that is aligned to all of those shades of gray in terms of maybe there's a different price, maybe there's a different term amount, maybe the loan term changes, the offer dynamics change as well. So it's not just yes and no. It could be yes, but with all of these different flavors associated with that. Right. And that's really important.
Jose Tagunicar: Yeah. When I first started my career, everything was like, I might age myself here. So forgive me, everything was like a decision tree and all these decision trees were so long and fast. And now it feels like with all the data that we have, it's a lot of bushes. A lot of bushes. And that's how agile banks have to be now because there's a lot of data out there.
Nathan George: Let's, let's, let's throw a real world scenario and I'm not going to take a side on this because it's a hot topic at the moment, but I think about the recent changes with all the buzz about the FICO score and the VantageScore and things like that. Right. Again, I don't want to offend friends and family in the space, but I with, from the perspective of a lender, let's say, let's say a lender who doesn't have a lot of free or available resources, it seems like there's some utility in automation to be able to address that. Maybe using that as an example, is there a way that the tooling that we've been talking about, what would that look like for a lender to use that to deal with? You know, oh, well, it used to be FICO, but now you can use Vantage and now they're in a price war. And which one's cheaper? I could really save on origination cost or, you know, whatever. What would the kind of capability we're talking about, how would that help?
Aarthi Muthukrishnan: I think, Nathan, what you're moving us towards is we've spoken a lot about the execution side of the strategy, right? But now it's about how do we learn from the past and get better, but not on a long timeline, very quickly, as close to real time as possible.
Nathan George: About a week between press announcements these days.
Aarthi Muthukrishnan: So how do we get there? Because the news cycle is super fast, things move so quickly. And how do we get there from like, okay, we have the strategy, how do we learn from that? Is it working? Is it not working? How do we change it? That requires again infrastructure to be able to collect the results of your implementation. The applications that are going through, what were the monitoring those applications and the approved accounts over time and being able to see some early signals on delinquency or risk metrics or profitability metrics, whatever your KPIs are. So you can act on it, but then to quickly act on it, you need a system that can do that. Testing the champion/challenger, maybe running 3, 4, 5, 10, 15 cells together at the same time. So you're constantly monitoring and optimizing. So it's a combination of both having a platform that is agile and can respond quickly, but also having the foundational capabilities around the data and analytics to understand the results and create those optimization scenarios. And that's where I think the capabilities with Stratyfy really shine and what we can bring to bear there.
Jose Tagunicar: Right, right. And just, Nathan, just going back to the whole FICO/VantageScore. What's going on? I kind of look at that as, what does an organization have to do to evaluate scores? Right. Could be FICO versus Vantage. It could be Vantage 3.0 versus Vantage 4.0. It could be FICO 7 versus FICO 10. It really doesn't matter to me. To me, it's the steps of validating which score is better. It's repeatable, and hopefully that's something that in the GDS infrastructure that repeatable analytics can occur. But I think what's more important, what's part of the argument with what score is better, is a client has to cross some sort of ROI threshold. Is it incrementally better? So you have to compare that to the cost. And so when I used to do these types of analysis, I would have had to order a bunch of archive data from the bureaus and do an archive analysis. And it took months to do that kind of analysis. And, you know, with platform at GDS, this could probably, you know, hopefully shrink the analytic time and the decision time so that, you know, that's just part of the. That's just part of the journey, making a change and realizing that ROI is the bigger part of the journey. So in as much as we can help clients shorten that, shrink that, that would be great.
Nathan George: While we're talking about execution and, like, there's obviously a lot of planning that has to happen for you to have the data where it needs to be and the format it needs to be and the connectivity and security and all those things vetted. I mean, that takes some setup time. But once that's in place, with respect to execution, one of the things we talked about early in the conversation today was trust. And I know trust from regulatory perspective and internal perspective, how do you control for, especially when you're using AI tools, how do you control for bias? I think you guys have some specific tests for that. Could you maybe talk about your automated bias testing a little bit and how that's helpful?
Jose Tagunicar: So with regarding bias. So bias testing is part of fair lending within compliance. And it's usually, you know, in my career, compliance has usually been a check at the end of a process. And so Stratyfy was founded in part to change that. And bias is one of the reasons why we exist here. So with our Unbiased module, testing moves into testing for bias, moves into the design process where analysts can see in real time if a change introduces a bias and disparity and fixes it before deployment. So compliance team can watch mitigation during development, analytic development and confirm fairness at the end and maintain also documentation throughout. So the compliance team, as I mentioned earlier today, they're usually at the end of the process. Now with AI, with black boxes, with data, with all these things, we see compliance moving up in the design and development process. And one of the things that we test for is real time fair lending. And so again there's a lot of things going on and we see that shift having compliance be in front of the process and during the process. And one of the ways that we mitigate this is through the Unbiased product that we have.
Nathan George: And it seems like as you expand AI models or you know, any kind of other kind of analysis that you're doing on your performance is that there's a lot of sophistication in the credit decision today.
Jose Tagunicar: Yeah.
Nathan George: And it seems like that sophistication right now a lot of the energy is directing that level of, same level of sophistication or maybe more towards like fraud detection for example. I imagine that bias testing might, might even be just as important, if not more so on the fraud side. And I definitely know there's regulatory impact further down the credit line. You know, the life cycle and you know, customer treatment, there's a default.
Jose Tagunicar: I think we're just scratching the surface on bias. A lot of people talk about Unbiased and fair lending in an originations context, but you kind of see this in fair lending testing. And bias testing can occur in any part of the life cycles. Marketing segmentation, like you said, fraud collections, who you're reaching out to, how are you assigned, is the way you're assigning credit lines or your loans or your APRs, is there any bias introduced there? So you know, we kind of see that shifting again to the front of the development. And so there's a need for, we found a need for automated bias testing. So you know, for us fairness isn't an afterthought, it's, you know, part of the reason why Stratyfy exists before you
Nathan George: put it in production. Right. Like I was just thinking, especially if you've connected into some sort of AI tooling and that AI tooling is for the most part it's a black box. Like what data was it trained on? And the answers that you, that it spits out, sometimes you don't know how it came up with those answers. And if you're basing any kind of, even if it's a non-FCRA decision like with fraud, why is it making those decisions and is that going to cause a problem for us later on? Is that being unfair to particular people? The black box nature of some of the particularly the LLMs is, can be daunting. So that's interesting. Let's. Let's move. You know, we, before we talk about the future, I'm going to flip the agenda around a little bit. I'd like to talk about because I think we're all part of it. So I want to talk a little bit about the reason for our partnership and then we'll move to, you know, some future prediction stuff. But when it comes to the reason for the partnership between Stratyfy and GDS, give me a few sentences on why from the Stratyfy perspective. And then from the GDS perspective, I
Aarthi Muthukrishnan: think I spoke a lot about what our core product that GDS is really good at implementing credit strategies with speed and precision. But then how do we make it so that our customers can actually improve that strategy, not just implement what they have? And that's where the obvious choice is really the solution that we see with Stratyfy. So today our platform, you can implement the credit strategy, you can monitor it, you can see what's happening. Yep. I booked so many people, I declined these many. This is where my distribution of my population looks like. But you're not really learning from it. Right. And then once you learn from it, how do you then we can implement it back. But what are those new things that you would want to test? That's where we find that Stratyfy with their product around the PRE engine where you can put some objective functions and say I want to optimize for profit margin or I want to optimize for a combination of an approval rate and a loss rate, for example. And for the data to then be crunched in the background based on all the data that we are seeing in our systems flowing through the system, the actual applications and the approvals and declines of lenders going to the GDS system, getting crunched through the algorithms that are behind Stratyfy solution to then come back and say here's how you should optimize your rule set and then take that new optimized rule set and then set it up as a challenger and test that within the GDS system. It's the full circle. Observe it for six months, see if it's working, maybe then promote it to become your champion strategy. That is the missing piece of the puzzle. We have not had so far and our partnership with Stratyfy really completes that for us.
Jose Tagunicar: Yeah, that's great. So I think part of the challenge is that I think we mentioned earlier is that tech and analytics have been kind of mysterious to a lot of the small mid sized lenders, specialty lenders, lenders that are fintech lenders, and lenders that are just kind of coming into the market. So when they realize that tech isn't as mysterious, that they can use it and they can be confident about really also helps them grow. And so with what Aarthi has described, with what the GDS platform looks like, Stratyfy and GDS gives relationship driven lenders a tool to test, measure, understand decisions. In our conversations with GDS, through the journey of this partnership through the PoC, through a lot of conversations about what PRE and our decision management module looks like in the market that we play in, a lot of the clients, they don't have armies of data scientists, they don't have armies of data analysts. But our tool, our product really is business analyst driven. When Aarthi talked about champion/challenger and optimization, we bring that power down back to the business analysts who are creating the business strategies and that they need something faster, quicker, better than what they have today. So this partnership actually makes a whole lot of sense for all of us here.
Nathan George: So it's kind of like you're marrying
Jose Tagunicar: the
Nathan George: design and execution of origination, workflow or credit decision with the monitoring, testing and improving with like a push of a button, right? You're, you're completing that circle. Take what you're doing today, then learn from it, optimize it, execute on, test and execute on it all within one platform without having to extract into a big Snowflake cloud or some other analytics tool. Right?
Aarthi Muthukrishnan: I kind of think of Stratyfy as kind of the kind of like your risk analyst in a box. Like, you know, if you don't have a big risk team to go and crunch the numbers and do run the analytics and come back to you, this is a more platformed, automated way of, of doing that on an ongoing basis.
Nathan George: We're getting close to the, the end and before we wrap up, I want to add one more question for both of you. Let, let's keep it short. I don't want to keep our, our, I don't want to keep it too long. But when you look at the future, and by future I mean near term future, the next year, the next two years, what are some of the most exciting things that you see for the GDS and Stratyfy together, right? Around the corner for us. What, what are some of the new things that, that maybe you could tease a bit about what's coming and how does it help our clients?
Jose Tagunicar: We are very excited about the future because we at Stratyfy, we have this idea about, as I mentioned earlier, kind of like democratizing analytics. Everyone does champion/challenger, right? Everyone does this A/B testing, so on and so forth. Optimization is not that racehorse. Optimization is a leap, okay? We're testing thousands and multiple scenarios all at once. And we're telling clients or we're working with clients and showing them where that finish line is. And a lot of the larger banks have done this for the last 25 years. And they have data, they have their system, they own everything. That's why they're able to do it, right? But now with GDS and with Stratyfy's methodology, we're able to have the, the smaller clients, the mid sized clients be able to do that. And so lately we've been working with a lot of the GDS folks and talking about different opportunities and different use cases. And so the partnership is starting to really gain some steam in really talking to the clients and the clients saying, hey, I have that problem, right? It's not the shiny, you know, they thought this shiny toy, but now it's within reach. So, you know, future. I'm really excited about the future. I'm excited about the roadmap that we have, the product roadmap that we have automating bias testing, going into different use cases. I think it's a fantastic feature. And because we are nerdy, Aarthi. Yes, we are nerdy. We love the data that you have. I mean, plethora of data. Everyone talks about alternative data, everyone talks about this data. So we can't wait to get our hands on.
Aarthi Muthukrishnan: Feels like it was meant to be. It's the perfect two sides of the coin. That's how I think about it. When I came to GDS, I saw how capable our solution is in terms of all the different things you can do to implement a very complex strategy and tune it and tweak it and keep going. But what was missing was how do you analyze all the decisions you made? How do you improve on that? And that's what the solution brings to bear. And I'm just super excited about how we can really help our existing customer base. But also for new prospects who are coming through the door who maybe have a very simplistic strategy, it's easy for them to get started with a solution that helps them think through their current strategy, figure out where the gaps are, how they can make it better before they go down the path implementing it in our solution. So all of those different aspects they can connect to our platform in so many different ways. And this gives us another way for new customers to to join the GDS and Stratyfy ecosystem.
Nathan George: Well, it's interesting to see what's ahead. Hopefully we'll have a follow up podcast next year to talk about some great case studies resulting from our capabilities. Really appreciate all of your time today. That wraps it up today for today's episode of The Lending Link. A big thank you to Aarthi Muthukrishnan, the Chief Product Officer at GDS Link, and Jose Tagunicar, Head of Product Strategy at Stratyfy. Again, thank you both for joining. Appreciate your insights on AI-driven decisioning and transparency and the future of credit innovation. It's been really interesting hearing about not just making faster credit decisions, but also about making them smarter and more optimized for your company's goals. It was really great to hear about the bright future ahead for our partnership and how we're combining the ability to not just execute on your credit decision strategy, but also to learn from it, to optimize and to put what you've learned into effect quickly in response to changing market conditions. If you'd like to learn more about GDS Link and Stratyfy partnership, you can visit our website or check us out on LinkedIn. We regularly post about upcoming webinars and podcasts. Thanks again for listening to The Lending Link and be sure to subscribe on Spotify, Apple Podcasts, YouTube, or wherever you get your podcast. Stay tuned for more conversations that challenge assumptions and inspire smarter thinking in the world of lending. Until next time, thank you very much.
Tune in to discover how this partnership equips lenders with sharper decision-making tools and real-world insights they can put into action.
From the episode:
- See how the GDS Link x Stratyfy partnership works in action — download the full overview (insert once approved).
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About Stratyfy
Stratyfy is an industry leader in decision optimization for financial institutions, empowering lenders to make more accurate, efficient, and fair decisions. Stratyfy was named a 2024 Banking Tech Awards USA winner, 2024 Benzinga Fintech Awards Best Lending Solution finalist, and 2023-2024 AIFintech100 honoree. Learn more here.
About GDS Link
GDS Link makes modern lending simple. Our real-time decisioning platform integrates over 200 data sources with advanced analytics, enabling lenders to make fast, data-driven credit decisions while mitigating risk. From loan origination to collections, GDS Link provides seamless automation, policy monitoring, and AI-powered insights to help financial institutions optimize lending strategies, stay agile in a competitive market, and drive better outcomes. GDS Link was recognized as the Leading SaaS Decisioning Platform 2025 and Real-Time Policy Monitoring Innovators of the Year 2025. Follow GDS Link on LinkedIn here.