The Nomis Narratives

Shaping the Future of Banking with AI

Standing before the audience at FIS Emerald 2025, I felt a sense of shared curiosity and urgency. AI isn’t just a buzzword anymore. It’s a tool we, as an industry, are learning to wield to solve real-world challenges. But with any powerful tool comes responsibility, and in banking, that responsibility is heightened by the need for trust, security, and precision. 

AI has already made significant inroads into banking operations, powering everything from IT support to chatbots. But there’s so much more it can do. My focus wasn’t on the theoretical; it was on showcasing tangible ways AI is cutting through complexity and helping banking leaders make better, faster decisions. 

Bridging the Gap Between Potential and Execution 

AI adoption is booming, but as I shared during the session, that growth is uneven. While 85% of banks believe generative AI will transform their industry, fewer than 10% have a concrete roadmap for how to get there. The tools exist, but many institutions find themselves stuck in the gap between potential and execution. 

This gap isn’t just about lack of resources or expertise; it’s about prioritization. Banks have primarily used AI for operational efficiencies like document review or process automation. But this focus doesn’t address the critical need for insight at the decision-making level. That’s where untapped opportunity lies. 

At Nomis Solutions, our mission is to close this gap—not by automating human decisions, but by equipping professionals with deeper, faster insights. It’s about using AI to gather, analyze, and surface critical data, so leaders can focus on making informed, strategic decisions. 

A Deeper Look at Nomis’ AI Functionality 

During my presentation, I showcased some of the ways we’re putting this philosophy into practice. These aren’t just concepts or prototypes; they’re tools banks are using right now to confront real challenges. 

1. Uncovering Balance Sheet Insights

One of the highlights of the session was our AI Business Analyst, a tool designed to tackle the complexities of deposit portfolio management. Picture the typical workflow required to analyze account performance. It often requires sifting through massive datasets, making manual comparisons, and interpreting trends. This process can take days, if not weeks. 

With the AI Business Analyst, we’ve compressed that down to seconds. By integrating AI into our platform, we enable instant analysis of portfolio behaviors. For example, during the demo, the AI identified elevated attrition rates in new accounts and highlighted regions where pricing outperformed expectations. These insights don’t just save time; they empower portfolio managers to respond to trends before they escalate into problems. 

What’s more, these insights will soon be available in users’ inboxes, complete with actionable narratives and visual charts. This isn’t just automation for its own sake; it’s automation with purpose, designed to help bankers focus their energy where it’s needed most. 

2. AI-Augmented Price Optimization  

Pricing strategies are another area where complexity reigns. Many banks struggle to find the delicate balance between optimizing rates for profitability and maintaining competitive offers. Traditional methods can be slow and rigid, especially when dealing with multiple variables like risk tiers or regional preferences. 

That’s where our AI-enhanced price optimization tool comes in. During the presentation, I demonstrated how this tool evaluates and challenges the user-defined optimization constraints, such as rate ceilings or balance growth requirements. It then tests alternative strategies, providing data-driven recommendations to improve profitability. 

One example I shared involved loosening rate constraints. The AI ran multiple scenarios, showing how slight adjustments could open up opportunities for increased net interest income. What would normally take days of manual iteration was distilled into minutes. This isn’t about taking the decision out of human hands. It’s about empowering teams with information they can trust, enabling them to be more agile and confident in their strategies. 

3. Commercial Exception Management 

One of the more nuanced challenges in banking is managing exceptions for commercial clients. Every exception request—from rate adjustments to loan terms—is a balancing act between maintaining relationships and preserving profitability. Historically, this process has leaned heavily on subjective judgment, leading to inconsistencies and inefficiencies. 

Our AI-driven exceptions management tool changes that dynamic. During the session, I demonstrated how the tool aggregates key data points, from customer behaviors to overall relationship value, and provides a comprehensive recommendation on whether to approve an exception. 

What’s particularly exciting is how the tool goes beyond binary yes-or-no answers. For instance, it might suggest granting a rate exception on a term deposit—but tie it to a commitment to accelerate an upcoming loan renewal. This kind of holistic thinking, powered by AI, helps banks manage relationships strategically rather than reactively. 

4. Model Monitoring and Documentation

On the backend, we’re also using AI to make our own processes more efficient. For example, my team spends a significant amount of time monitoring the performance of behavioral models. This involves analyzing stability metrics, comparing challenger and champion models, and ensuring alignment with regulatory standards. 

We’ve built AI tools to handle much of this heavy lifting. These tools automatically flag performance issues, highlight areas of concern, and generate in-depth documentation. This doesn’t just save time; it ensures rigor and consistency, ultimately benefiting the banks we work with. 

Compliant AI Isn’t Optional 

While the tools themselves are impressive, they wouldn’t matter if they weren’t built on a foundation of ethics and security. AI isn’t just a technical challenge; it’s a trust challenge. 

That’s why every AI feature we deploy is designed with privacy and security at its core, with single tenancy, opt-in feature flags, and guardrails against hallucinations. These are deliberate choices, designed to ensure that banks feel confident in adopting AI without risking their data or their customers’ trust. 

Unlocking AI’s True Potential 

The session at FIS Emerald 2025 wasn’t just about showcasing tools. It was about starting a conversation. How do we, as an industry, move beyond the hype and focus on what truly matters? How do we ensure that AI isn’t just a tool for efficiency but a catalyst for better, more ethical decision-making? 

For me, the answer lies in partnership. AI can’t fulfill its promise on its own; we need to work together to shape its trajectory. Whether it’s through collaborative innovation, thoughtful regulation, or shared best practices, the choices we make today will define the role AI plays in banking tomorrow. 

If there’s one takeaway from my session, it’s this: AI isn’t here to replace us. It’s here to help us see more clearly, think more critically, and act more decisively. And with the right tools and the right approach, the future of banking can be as dynamic and adaptive as the world we’re all navigating together.  

If you’re ready to turn AI potential into real results, get in touch at sales@nomissolutions.com or connect with us via nomissolutions.com to speak directly with our experts!

Written by: Wes West, Chief Analytics Officer at Nomis Solutions