Future-Proofing Financial Services: Aditya Khandekar CRO of Corridor Platforms on Decision Automation and Gen AI in Banking

In this episode we speak with Aditya Khandekar CRO of Corridor Platforms about how decision automation, risk management, and Gen AI are reshaping financial services. Learn how Corridor optimizes customer journeys, enhances decision quality, and prepares banks for a digital-first future with human-like AI experiences. Discover strategies to thrive in an evolving, tech-driven financial landscape.

Key Words:

• Financial Services
• Decision Automation
• Risk Management
• Gen AI in Banking
• Digital Transformation
• Customer Journey Optimization
• Corridor Platforms
• Data Assets
• Decision Quality
• Future of Banking

Transcript

Hello and welcome to Banking on Information. Today I have Aditya Khandekar, Chief Revenue Officer at Corridor Platforms, with me. Aditya, welcome to the podcast. Thank you, as usual, great conversations we had in the past and now we're doing it in a podcast. Looking forward to it. As we've discussed, I always start with that very important question about what is your why? So Aditya, why do you do what you do? I've been very passionate about technology, finance, analytics, and let's say for the last seven, eight years around risk management and how all of these confluence comes together, where you actually deliver strong impact. across the customer life cycle from prospecting to underwriting to customer management. And while you're doing this in a rapidly evolving digital landscape, that is today's reality for banking and credit unions.

So being able to deliver this impact where a combination of intuition, math, technology, putting it all together and bringing it to the benefit of the customers and consumers, members, and commercial customers-that's what drives me and makes me passionate, and also where I'm going to go today. Great! So that is a great way to realize that that's your why, and why you're doing this every day. Help us understand what use cases Corridor Platforms helps to solve for their customers. What do you do there, and how do you put that why to work? Sure. So the genesis of Corridor was that when we looked at how decision management is being done today, especially in the digital marketplace, Lots of inefficiencies, a lot of silos between data models and strategies, handoffs being inefficient, errors, costly errors coming in.

And when you're operating in the digital world, you have to be real-time in the way you're able, or quasi-real-time in the way you're able to build decisions, to evaluate, incorporate market data, both from what you're seeing in the digital marketplace as well as your competitors, incorporate that while moving quickly. Not falling off the guardrail. So I use the word speed with safety. You have to move fast. Otherwise, you're going to lose your potential customers across the lifecycle, not just an acquisition. And safety, because regulators are watching every step. And those who are best in class in both these dimensions are going to succeed. And that's kind of the genesis of what Corridor enables us, what Corridor enables for clients in the marketplace, whether it's tier one institutions or mid-market.

So you help customers make credit decisions and tough decisions fast, but making sure that you do it in a safe way so that you're making the right decisions, but hopefully fast for the customer. Yeah, and I'll add a few more businesses here. So it's not just about, if you think about like the iceberg, where the tip of the iceberg is acquisition. You've got a whole, you know, three-fourths of the iceberg is underwater. What you're really trying to optimize is not just your acquisition decision, but you're optimizing across the entire lifecycle. You're optimizing the member or customer journeys and fulfilling their needs while meeting your profitability requirements and returns that you want to give your stakeholders. So it's actually a customer optimization and a member optimization problem, not an acquisition problem.

And when you look at it from that lens, you realize. And right from the prospecting side to getting the right prospects in, to providing the right choices of products and offers to customers and commercial, and then being able to downstream, being able to manage that relationship so that you're constantly upping your game with your clients and maintaining them profitably as they stay with you for many years. And also the retention aspect and also loss management aspect. When you optimize across the entire life cycle with best-in-class decisioning, that's when you start to really get to a rarefied zone. Very few institutions are able to do that, but the platform that we've built is really enabling you to optimize an entire journey not just acquisition. That's great.

Now, can you tell me a story of how you deliver that value for your customers? So think of one of the customers. You don't necessarily have to name them, but maybe walk me through how they are getting value from the solution that you deliver for them. Sure. And I'll give you an example, one tier, one client, and one mid-market client without naming them. So for the tier one client, our focus was really how can we improve the overall process of modern risk management processes? Which, you know, when they're doing it at scale. So when you're operating at, you know, thousands of features and hundreds of models, in this example, in a marketing function, you have to be, you have to design the workflows in a way that 50, 60 data scientists, data engineering teams, your second line functions, all of them orchestrate and synchronize each other to make this entire thing work at the pace of digital.

So that was the problem statement there. And when you look at it from a mid-market perspective, I think the fundamental problem statement that we were solving, and we solved this with a couple of mid-market clients, is mid-market clients have to upgrade their decision-making capability, not just for the digital world, but also to compete effectively against tier ones and fintechs. And if they don't do that, they get adverse selection. Now, I'm going to make a controversial point here. For mid-market, not having that level of sophistication of a CAP1 or an Amex can actually be an existential crisis if you're not careful about this, right? Or if you're not thinking about this problem carefully. So fundamentally, that's the kind of issues or those are the kind of issues that we are solving in the mid-market for clients where we come in, look at your data lakes; we see whether we need to update or enhance your data lakes.

A former decisioning perspective: we build the entire decisioning layer or the platform is installed in your environment, flexible deployment options. But then we also curate analytics on the platform, but that could be custom models that could be streamlined strategies, and then kind of get you best-in-class. I want to say one or two points there-there are two parts of this in my head: one is decision quality, second is decision automation. These both are related, but they're two two dimensions in that sense. Right? By the way, decision quality leads to decision automation or enables decision automation. But just because you make great decisions doesn't mean that when Ruger gets a great offer, but he gets it, let's say, 10 seconds later than a Goldman Sachs made an offer-as an example. Well, you lost an opportunity.

You had great approval rates, but you didn't have good acceptance rates. So those are the kind of dynamics where decision automation is as important as decision quality. And that's where the ability to look at it as a system, where you're building, experimenting, testing, putting stuff into production, and streamlining your strategies is as important as having best-in-class models. And I believe in today's environment, even more streamlining the strategy is going to be very critical. The ability to build, experiment, put into production, monitor, make tweaks, get it approved, put into production, rinse and repeat that process is going to be very critical for success, whether that's tier one or mid-market. And those are kind of the fundamental principles of what I call business, yeah.

So you help your customers make decisions faster and make better decisions, so that they get auto-approval, which then results In more take up um which then results in more more revenue with less loss and a better customer experience so that is a great way to combine all those things together That is correct And then going back to the earlier point we were speaking about once you've got the best in class so to say high quality applicants through the door and booked on your balance sheet then you get into the point of how do I maximize the benefit that my members and customers are getting Because they have to see that value from you So being proactive, not just at the door entry, but also in an ongoing relationship and also protecting yourself as an institution from a loss and a delinquency perspective.

So you have to be best-in-class in all of these, and your operational processes have to run for decision in a way that supports that. And that's essentially what Corridor supports. Yeah. Now, I like to do this thing called future stalking, where we look at a possible future. We don't know what the future is going to bring us, but we can think about it, what a possible future could look like. So let's think 10 years out, 20-34. What in your mind does the banking industry financial services industry look like and how is it different from today a couple of dimensions to that let's pick the headline dimension i think you're going to see adoption of gen ai capabilities in banking in a massive way we are literally putting the toe in the hot water right now right i think there are low hanging fruits of opportunity that we're seeing for example how do i do call center optimization, for example, today.

So there are certain set of requests where humans are not required. And we're still a little bit in the mode of decision support, but not really giving the AI engine the ability to make that decision and also execute on it. We're not there yet because of all the regulatory scrutiny and the need to make sure that the guardrails are well put in place. But I think Gen AI at the threshold will keep increasing. Its presence in all the different operations of the institution, you're going to see heavily digitized, more heavily digitized banks in 10-15 years from now where it will feel human-like but through the Gen AI capabilities and most sophisticated intelligence that have been built in, there will be one efficiency's to be had which will hopefully drive profitability and sustainability of mid-market.

As a prediction. And on the other hand, the quality of service itself will dramatically improve for the members, customers, and your commercial customers. Just think about it, Rutger. How often have you been very happy with your institution? That's a shrug, right? That's a shrug. What if we could get to a point where on a systematic, scalable basis, let's say if banks were being scored on an NPS, what if you could get systematically like a six and a half or seven out of ten that would be a very nice world to live in right? Yeah, that's kind of the thing I think-that a combination: a smart combination of quality data, the right type of regulation, guardrails, and the combination of AI engineering.

You kind of put that mix into working, and then obviously forward-looking leaders and institutions, whether they're on the business side, the risk side, and the technology side, working in cohort. When you kind of do these things, there will be an opportunity to be really, really, very, very customer-focused with high-quality service at affordable pricing, and that should reflect into a lot of things in terms of not just profitable profitability for the institution but also pricing and all those kinds of benefits that that pricing and flexibility that members and customers could get. So you're seeing a world where artificial intelligence Feels very human-like and gives you a great experience in financial services. What do we need to do today if that future is going to be real 10 years from now?

What do we need to do today to get ready for that future to be able to deliver that in 10 years from now? I think to answer that question, a big element of all of this is sound investments into data assets and decisioning, at least from the lens that I wear, data and decisioning assets. Putting these into play today is going to be very critical to set the foundations to achieve that target state. But the target state requires a confluence of process, smart people, and talent, and very customer-focused workflows. Driven off sound risk management principles, data, and technology. So when these three things kind of come together, we have the data, technology, and decisioning capabilities today; I'm sorry, risk management principles today in place, but the ability to kind of orchestrate that better for the digital world.

If those are put in place today, then it'll really enable that 15-year future state that we talked about earlier. So making those investments today. for the digital and build first principles ground up for digital right there's one thing to say I jerry-rigged my current process of digital and other one is to really rethink that process from a digital perspective and I'm wearing the decision management lens so rethinking your workflows and your experimentation approaches and your approval workflows and all of those workflows even the client workflows looking at them from a digital lens first principles is really what will enable that target state let's say Yeah, no, that is great. So, you're going to Corridor Platforms, you are actually getting ready for that future and you're making sure that all your I's are dotted and your T's are crossed so that when we get ready for that AI-driven, human-like future, that you are ready to go. That is a great way to end the conversation today. Thank you very much Aditya for being on the show today. If you have any questions for Aditya or myself, feel free to reach out to us on LinkedIn. And until next time, choose to be curious. Thanks Aditya. Thank you.