One of the reasons the banking industry is fascinating is the disconnect between its reputation and its reality. Banks are widely seen as sleepy, old institutions that sit atop 40-year-old tech stacks while comfortably running the trusty ol’ 3-6-3 business model (1). The reality, of course, is starkly different. For thousands of years now, banks have been at the cutting edge of innovation. They were there when fiat currency was first developed, they were first in line to make use of giant mainframe computers, and they were among the first to move real, actual commerce to the internet. Now they are again at the front of the line in the age of AI, churning out some of the first high-volume use cases that move beyond parlor tricks and into true, positive ROI improvements to business models and customer experience.
So, what’s the secret to this under appreciated success with technology? It’s building on proven techniques and using well-established processes as a foundation. In this article we dig into how one of those tried-and-true tools, Flow of Funds Analysis, can be super charged with modern techniques and better data.
Flow of funds modeling is a bedrock of banking operations. It shapes deposit pricing strategies, loan growth plans, liquidity management, and balance forecasting. When leadership teams ask, "Which balances are at risk of runoff?" or "How sustainable is our deposit growth?" the answers often come from these models.
Traditionally, static models based on historical behavior have been the go-to approach. But with depositors moving funds faster and exercising greater choice, sticking to rigid, backward-based models exposes banks to unnecessary risk. What’s needed are tools that not only respond to market changes but anticipate them, enabling faster, data-informed decisions.
Most deposit optimization approaches isolate each product and build models and forecasts on how that product in aggregate will grow in response to price changes. This framework may have been good enough in the ‘90s, but it ignores the distinct dynamics of new client acquisition versus existing customer growth, cross-product cannibalization, and halo impacts of marketing campaigns.
The Nomis approach to deposit price optimization appropriately reflects the complexities of this modern world. When setting a teaser rate, product management teams need to think about not just how many customers will be acquired, but the marginal cost of near-term cannibalization and the long-run impact of acquiring “hot-money” customers. The Nomis Price Optimizer (NPO) behavioral models account for these second-order effects. NPO equips product managers with the tools and insights necessary to deploy intelligent price and offer strategies, aware of how these moves will holistically impact the balance sheet.
Nomis is leading this transformation with its innovative AI-powered agent, integrated directly into NPO. This agent doesn’t just analyze or execute models; it collaborates, challenges assumptions, and generates alternative scenarios in real time.
Think of it as a “second brain” for analytics teams. Instead of relying on a single forecast, the agent provides diverse model variants, identifies edge cases, and tests stress scenarios. It proactively refines strategies for pricing or retention, adapting to market changes as they occur. By constantly learning, this AI agent supercharges product management teams, vastly accelerating the time to insight loop translating data into actionable insights.
Embedding AI into flow of funds modeling unlocks a wealth of advantages for banks, helping them work faster and smarter. Teams can greatly accelerate their decision process, generating and analyzing multiple, complex scenarios in a fraction of the time. The skilled (i.e. expensive) personnel that own this process can spend more time thinking and making decisions, and less time fiddling with model inputs and software settings. This is not only more efficient, but also directly translates into enhanced agility. Especially in volatile markets, a great decision made faster is often more valuable than a perfect but slow decision: Nomis AI enhancements offer the rare chance at both, putting smart strategies out into the market before the competition has a chance to react.
Of course, banking is still a strictly regulated and highly leveraged business. Any decision making process that relies on statistical models is rightly put under the microscope from both examiners and internal risk teams. A significant portion of your analytics team’s time is spent in governance and model risk management. Luckily, this is another area where thoughtful application of AI can bring significant improvements. Nomis uses these new capabilities to model assumptions proactively, challenge their validity, and transparently document the various trade-offs as strategies are developed. Again, this is more than efficiency play; real-time governance is far more effective than the typical, after-the-fact approach that is focused on “papering the file” to satisfy regulators.
For mid-sized institutions with smaller analytics teams, the advantages are especially clear. Automating aspects of model development and validation empowers teams to do more with fewer resources while maintaining rigor and precision.
Nomis’ AI-enabled flow of funds agent is just the beginning. The next stage of innovation is focused on revolutionizing how predictive models are created. By simplifying and automating model development, Nomis aims to lower the technical burden on banking analytics teams.
These advancements extend beyond flow-of-funds applications. AI’s predictive power has the potential to improve deposit elasticity analysis, enhance loan runoff forecasts, and guide pricing strategies at all levels. By prioritizing ease of use and accessibility, Nomis is democratizing analytics, giving businesses of all sizes the tools to make smarter, more strategic decisions.
AI’s role in banking isn’t about replacing human expertise but enhancing it. By embedding AI into flow of funds analysis, banks can respond quicker, strategize smarter, and ensure decisions align with their goals, even in complex and fast-changing environments. This shift from reactive to proactive decision making is redefining what’s possible for financial institutions. Who knows, you might even get to outgrow that ill-deserved (but still very much present) stigma of being a stuffy, boring banker!
If you're ready to explore how AI-augmented flow of funds modeling can elevate your bank’s strategy, now is the time to act. Connect with Nomis to request a personalized demo and discover how this powerful solution can transform your analytics workflows and drive tangible outcomes for your organization.
Get in touch at sales@nomissolutions.com or connect with us via nomissolutions.com to speak directly with our experts!
(1) For those too young to have heard this, it is a tongue in cheek reference to running a bank by paying 3% to depositors, loaning it out at 6%, and hitting the golf course by 3pm (https://www.investopedia.com/terms/1/3_6_3_rule.asp)
Written by: Wes West, Chief Analytics Officer at Nomis Solutions