AI-led transformation, interest rate impact, and enterprise performance strategy for financial institutions—insights from the frontlines.
Eric, as the Senior Director for Product Management at Strata Decision Technology, could you walk us through your journey and how your role shapes the direction of financial technologies and services?
As the Senior Director of Product Management at Strata Decision Technology, I lead the strategic development and roadmap for the Axiom Suite, which is tailored specifically for enterprise performance management within financial institutions. I oversee all aspects of product design and development, ensuring that we maintain high standards of quality and deliver impactful, innovative solutions to our clients.
Before joining Strata, I spent nearly 13 years at UMB, a regional bank, where I served most of that time as a VP/Finance Manager. In that role, I worked closely with senior leadership on profitability analysis and the budgeting and forecasting process, which gave me a deep understanding of the financial challenges and needs faced by institutions.
In 2017, I joined Syntellis (which was later acquired by Strata) and spent my first several years as a lead consultant. In that role, I focused on implementing enterprise performance management solutions and advising financial institutions on best practices to drive performance and efficiency.
With the role of AI becoming increasingly crucial in the financial sector, what are some of the key ways you foresee AI driving innovation in financial systems, fraud prevention, and customer personalization by the end of 2025?
As AI adoption grows, I anticipate it will play an integral role in how financial institutions continue to enhance the client experience. Specifically, AI will become a core enabler of innovation across three strategic pillars: financial intelligence, security, and personalization. In financial systems, tasks will become increasingly automated, freeing up finance teams to focus on strategic analysis. In fraud prevention, real-time machine learning models will enhance transaction monitoring and reduce false positives. And in customer personalization, AI will power dynamic experiences, offering timely product recommendations and financial coaching based on individual behavior and goals.
Financial institutions are facing several risks in today’s environment. What do you see as the biggest impact of interest rate changes on financial institutions, and why are they considered the number one driver of business model change?
Interest rates are the key driver of and the single most powerful variable in a financial institution’s earnings model. Changes in rates affect net interest margins, loan demand, deposit pricing, and balance sheet strategy. According to recent data from our 2025 CFO Outlook for Financial Institutions Report, leaders cite interest rates as both their top risk and top catalyst for business model change. With potential rate cuts on the horizon, banks are rebalancing toward non-interest income, optimizing product mix, and investing in scenario modeling to stay agile amid market shifts.
As banks and fintech companies continue to evolve, how do you envision AI’s role in reshaping traditional banking models and optimizing customer experiences in the next few years?
AI has the ability to transform the banking model from siloed, product-centric approaches to data-driven, customer-centric ecosystems. As fintechs continue to raise the bar on digital experiences, traditional banks must utilize AI to close the gap—enhancing speed, personalization, and engagement across the customer journey. To start, traditional banks can explore integrating AI to offer proactive insights, automate workflows, and deliver services on demand.
What major trends do you anticipate in the mergers and acquisitions (M&A) landscape for banks, credit unions, and fintech companies? What factors will be driving these changes?
We expect to see continued M&A activity in 2025 and beyond, driven by the need to gain scale, acquire digital capabilities, and respond to competitive pressures from neobanks and tech-enabled challengers. According to our recent CFO data, deals between credit unions and banks, and banks and fintechs, are accelerating. Regulatory softening and asset quality improvements are creating a more favorable M&A environment—particularly for institutions that lack the capital or talent to innovate organically.
The challenge of balancing innovation with regulatory compliance is a significant issue for the financial industry. How do you believe institutions can use AI and modern financial technologies to maintain this balance effectively?
The key to balancing innovation and regulatory compliance is embedding governance and explainability into AI solutions from the start. Financial institutions must work cross-functionally—bringing together compliance, risk, and data science teams—to design AI systems that are innovative yet compliant. Tools like AI risk frameworks, model validation practices, and transparent audit trails, for example, are becoming essential. Modern technologies should also enhance regulatory reporting and help mitigate risks rather than introduce new blind spots.
In your experience, how do financial institutions approach the integration of advanced AI tools into their systems? Are there any common barriers or challenges they face during this process?
Many institutions begin integrating AI tools into their systems through pilot programs in narrow use cases, like chatbots or fraud analytics, but often hit barriers when scaling. The most common challenges include fragmented data systems, limited internal AI expertise, and concerns around model governance. Successful adopters prioritize data infrastructure modernization and cross-functional enablement. Strong executive sponsorship and quick wins can also help build internal momentum.
As customer expectations shift and become more personalized, how do you foresee AI being used to enhance personalized financial services? Can you share any examples of how this is already being implemented?
AI enables banks to move from mass segmentation to true one-to-one personalization, marking a transformation in customer services. This includes predictive product offers, tailored financial health insights, and personalized communications. For instance, some institutions are already using AI to trigger nudges—like overdraft warnings, savings suggestions, or spending alerts—based on real-time account behavior. These micro-interactions build trust and deepen engagement, thereby enhancing the customer experience.
We know that financial institutions are under pressure to streamline operations and reduce costs. What role do you think AI will play in achieving operational efficiency and reducing overheads within these organizations?
AI is proving instrumental in reducing manual effort, enhancing accuracy, and speeding up processes for financial institutions. From automating reconciliations to optimizing branch operations and underwriting, AI helps shift resources from low-value tasks to high-impact analysis. According to our CFO Outlook survey, AI is already being leveraged for process automation and is expected to play a major role in back-office transformation by the end of 2025.
inally, as the industry adapts to new financial technologies and AI applications, what do you believe will be the most significant breakthroughs or changes in the next 5-10 years? How should financial institutions prepare for these changes?
In the next 5–10 years, we’ll likely see breakthroughs in autonomous finance, real-time risk forecasting, and AI-native business models. Institutions will need to prepare by investing in data maturity, building AI governance, and adopting modular platforms that allow for rapid innovation. The winners will be those who can balance scale with agility, innovation with compliance, and automation with human insight.
A quote or advice from the author: “As financial institutions continue navigating a rapidly evolving landscape, the path forward will be defined by how effectively they leverage data, access critical insights, and plan for the future. Leaders must prioritize breaking down data silos and enabling real-time access to decision-critical information. Just as importantly, strengthening advanced planning capabilities will be essential—not just for adapting to change, but for driving sustainable growth and long-term stability.”
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