The AI Lending Highway: Moving from Stop-and-Go to Cruise Control

AI is transforming the future of lending. But without the right foundation, it risks becoming another checkbox rather than a competitive edge. Most banks remain stuck in pilot mode with AI, with limited use, minimal integration, and disconnected systems. This session dives into what it truly takes to move from test mode to full throttle, and steer beyond automation to achieve process autonomy. Explore the core digital capabilities, including integrated journeys, omnichannel experiences, unified workflows and real-time data access, that must be in place for AI to deliver maximum impact. Discover how an AI-first lending platform can enhance customer experience (CX), drive revenue growth, and increase operational efficiency. Backed by real-world examples, this session will show you how to shift gears and make AI a game-changer.


Transcription:

Melinda Husman (00:11):

All right. Good morning. My name is Melinda Husman. I'm with American Banker. Introducing today's panel, our session, the AI Lending Highway, moving from stop and go to cruise control. Today's speaker is Greg Solans, who is the US Banking Center of Excellence Leader at newgen. Please welcome me in introducing Greg Solans. Thank you, Melinda.

Greg Sullins (00:33):

Thank you very much. It is great to be here with you all today. I put together a few slides. I know it's difficult in here to hear, but if any of you want some of these slides, you can email me afterwards or see me. Give me your business card. I'll be glad to get those. I wanted to just kind spice this up a little bit and get us going with this approach to AI lending and moving really from stop and go to cruise control. So let's get started. There we go. So the risk of doing AI lightly at this conference, we've heard a lot of you out there. AI is on your mind, but many institutions are approaching AI as more of a checkbox or pilot project with lack of real integration that's leading to pilot paralysis, if you will. Lots of experiments, but no sustainable impact and disc connected systems in those siloed data limits AI's effectiveness in your return on investment.

(01:45):

So the key tip here is without the right foundation and commitment, AI risk really becoming a buzzword, not a business driver for your organization. So the opportunity really for your organization is to move from automation to autonomy, and I'll talk you through that, right? Because this is going to be key and hopefully this story can tie together a lot of what you've heard here at this conference. So automation really is about speeding up task, right? But autonomy is about self-driving processes that learn, adapt, make intelligent decisions. So today, here today, we'll explore how your bank can shift gears from simple automation to AI led process autonomy. So the road ahead is more about really incremental gains and about the shift. Now many of us here really are involved in AI in some way, and we talk a little bit about stop and go, but some of us are really at the starting line trying to figure out what does all this really mean as we think about the journey ahead and how can we hear this promise of being faster, better, more compliant, all of these things that we can do with ai, but how do we get there?

(03:20):

So let's talk about some numbers today. Ai, we have a lot of pilots going on. They don't really have a platform out there. 68% of the banks that we see are just beginning their AI journey. They have limited scale beyond pilots and capability. So we have siloed systems. 54% of the financial institutions really identify this as a big concern, major barrier in their innovation efforts. We have manual heavy workflows. Nearly 40% of our financial institutions have fragmented systems, labor intensive, heavy workflow. And then that's really, we also see a third of the customers indicating if they had better access to channels and information, it would improve their ability to obviously stay and grow their relationship with their financial institution.

(04:26):

So as banks, where are we in this? Stop and go. Why is this the issue? Number one, we have disconnected systems. Systems. AI really can't thrive in an environment where we don't have consistent connected data sources both across your loan origination, your risk, your servicing, compliance teams. It's all got to be connected. Number two, there is a compliance gap. We see that in that confidence from regulatory concerns to your internal behavioral concerns. Somebody can you go too many button, go backwards, going the wrong direction. There we go guys. So AI insights aren't really actionable if they can't flow across the organization.

(05:21):

There's that pilot paralysis, that fear. Are we doing something wrong? Is really unclear. Return on investment, lack of the IT and the business, collaborating and getting on the same page. And then finally, this gap between automation and transformation. Automation is about doing things faster, but transformation is really rethinking your process, and that's really where the AI can come in and really be a game changer for you. Now as we look at this and on this journey, where is your organization on this AI journey? Today, you may be down here, pilot or maybe a partial implementation or scaled AI to ultimately autonomous lending, but most are in the middle. Most of the banks and credit unions today are right in the middle. But with the right platform, the right strategy, you can really shift gears and realize some real business impact. So let's define some terms though for you on automation versus autonomy.

(06:40):

So automation typically we execute for to find tasks. Many of you in your organization today, do that today in an autonomous lending environment. You're learning adapting dynamically. We have requires human design and oversight versus minimal interaction. This third point is really around rules, right? We use a lot of rules-based decisioning and we're deploying now at NuGen the AI machine learning models as additive and increasing speed to enhancing intelligence. The key tip here is automation makes you faster, but autonomy makes you smarter. Think of the difference, if you will, of driving your automobile, setting it on cruise control versus a self-driving automobile. That's what we're talking about here between automation and autonomy. Everybody with me? So automation is a step, autonomy is a strategy. So again, think of AI like electricity. Initially it powered individual machines, but over time it transformed entire industries. The same is true now with ai, but AI shouldn't just accelerate existing process.

(08:13):

It should reinvent it. So I'm going to give you four reasons why process autonomy should be the future of lending. Number one, scale. Human-driven workflows really limit the throughput, but autonomous processes scale through cycles effortlessly. Number two, customer expectations. We all see this every day. 24 7 demands, expectations and autonomy enables that. Number three, cost and compliance. Autonomous systems can reduce your error rates, provides you better audit trails, identify flags and compliance issues instantly versus days later. And finally, a competitive advantage. Banks that build adaptive self-improving systems will dominate with speed, personalization and margins. So let's pause for a moment and think about what are the signs that you're still in pilot mode in your organization? If any of these sound familiar, you're likely stuck in automation, not autonomy in your organization. You've got localized use cases. AI is only applied to one function, maybe fraud, maybe OCR, but not across the organization. You got dependencies on human oversight. Siloed AI initiatives are you're not collaborating across the end to end journey. Static workflows, maybe lagging data integration. You don't have real time data. And the ROI is unclear. We just heard that repeatedly in the previous sessions. If you've got any of these, you may be stuck in automation not going looking toward autonomy.

(10:17):

So as we think about this maturity path on this AI lending highway, we want to move from automation to intelligence to autonomy. Number one, automation, rules-based task execution. At nian, we work with many of you out there right here on this stage alone right now. But then we moving to number two, intelligence, AI assisted decisioning. Some of you maybe are getting a 65% approval rating on your loan products. We're adding the second layer of intelligence and moving that to a 68, 60 9% approval rating, significant lift. And then third is autonomy. Self-improving workflows embedded across your origination, your risk areas, your servicing areas.

(11:16):

You can't do all of this though without the right foundation, right? So there's four pillars of AI lending that really are critical here. Number one, integrated journeys, right? You got to orchestrate your end-to-end. Have a platform that can enable end-to-end journeys from lead management all the way through to servicing processes. Omnichannel experience, we hear this time and time again, the ability for many of your customers to start in one channel and finish in another channel or vice versa, is paramount. Unified workflows, all the parties involved, whether it's your loan officer, your credit analyst, your loan operations, your compliance, everything unified and integrated and collaborating and extending that to your third party providers like appraisers and your attorneys. And then of course, real time data access. Without this foundation, you want to have limited ability to execute. And these are some things that we start to work on time and time again.

(12:38):

So with that said, what is an AI first lending platform? First and foremost, it's the end to end journey that I just talked about from lead management, qualification, decisioning, underwriting, processing, closing, and then on into loan monitoring. It encompasses an omnichannel on the left hand side that works across your branches, your online, your mobile, your call center. It encompasses integrating with your core, your CRM, your credit bureaus, other third party services for KYC. It includes a native technology of cloud native microservices, low code API enabled. And then we layer in and it's fully integrated, is the AI studio where we have rule and model battery, intelligent document processing and gen AI agents all collaborating for improvement. So many vendors claim to be AI powered, but true AI first means AI isn't bolted on. No, it's embedded throughout the architecture, the workflows, the decisions, and that's what Nugent's platform enables.

(14:04):

I'll just share a few use cases that we see time and time again here on AI that helps powering intelligent lending. We're seeing consistent intelligent document processing, lots of use cases in both commercial small business loan origination where we're automatically identifying documents, automatically extracting information and summarizing those very sometimes 300 page credit reports. That's a task. It takes hours now, seconds. We're seeing AI-based risk scoring and fraud detection. This gets real exciting when we layer in the machine learning models and the risk profiles in addition to your traditional scoring methodology. And then we're seeing next best action, and that is that personalization. What is the probability that this customer is going to accept this offer and then move to the next opportunity along with those next best actions is the probability of default. But AI isn't just for decisions, it's for engaging your customer. The platform adapts messaging, product fit and timing to for each and every customer.

(15:27):

So why does all that matter, right? Well, it matters because you got to get to a 50 to 70% reduction in processing time. You can see a three x improvement in straight through processing, which can translate to much better 20 30% improvement in customer experience. Reduce your risk of fraud losses, and increase your share of wallet through your personalization efforts. With Nugent's AI first lending platform, you really don't just try to digitize the lending journey. You try to elevate it. It's really a launching pad for faster growth, sharper insights, and really smarter customer engagement, if you will.

(16:19):

Lemme give you a real life case study. We all like that. The challenge, this was a regional bank here, manual loan origination process took seven days on average. They had high seasonal volumes, customer complaints for slow approvals and lack of transparency. They implemented Nugent's AI first platform with intelligent document processing and automatic workflows. We incorporated real-time credit scoring integrated into the decisioning with an omni-channel application flow result. Loan turnaround, which were seven days under 24 hours straight through processing, went from 35% to 80% Customer satisfaction scores rose as well as the productivity and the ability to do more with the same staff in that organization. So it was game-changing for that organization.

(17:21):

So the key takeaways from that success story, really center on AI first platform enables significant acceleration of loan processing. Personalized AI engagement drives higher conversion and retention efficiency gains, which freed up staff to focus on more higher value functions and measurable return on investment, all the things that are important to your bank and all the stakeholders there as well. So these wins are really a blueprint showing you how to move from a fragmented pilot to an enterprise skill, AI powered lending environment, driving growth, efficiency and customer experience. So what I'd like to do is share with you here in more of a summary fashion, what we've just talked about. And this would be your five steps for you to take back to your organization and work and brainstorm with your leadership teams. Number one, evaluate where you are today. Conduct a thorough assessment of your current AI maturity.

(18:41):

Are your AI pilots isolated or integrated? Right? What digital foundations like data workflow channels, do you have in place? Identify gaps in your technology skills and processes. Number two, define very clear AI goals. And those should be centered around the customer experience, around your risk appetite, around your growth objectives, and your efficiency of your bank or credit union. Number three, build those digital foundations in parallel. You can do that. You can really focus on the integrated. Don't chase the AI without a solid foundation. A lot of people are trying to do that and they don't have the integrated loan journeys. They don't have omnichannel unified workflows and real-time data access. You can do a lot of these things in parallel. Number four, choose the right platform partner. Select a partner that offers a cloud native, scalable technology supports. A broad AI ecosystem, has a proven success with lending institutions and provides flexible integration of your existing system to bring it all together.

(20:11):

And my final thought on that is really organizational alignment. We see this so many times, foster good collaboration between your IT teams and your business teams help the IT teams ensure the infrastructure, the security, the integration is ready, the business drivers of the use case prioritization, user adoption, as well as cross-functional governance and communicate your early wins and momentums and support throughout that journey. So those are five steps. I wanted to leave you some tangible takeaways. The AI Lending Highway is really wide open for those ready to accelerate with the right roadmap, foundation, and partners, your bank can move from stop and go to full speed ahead. It's my pleasure to be here with you today. Thank you very much and love to talk to you after this. Or if we have time for any questions, I'll take 'em. Thank you.