As an authority on payment ecosystems, Scotty Perkins has witnessed firsthand the transition from traditional batch processing to the high-stakes world of real-time finance. Having led product management at ACI Worldwide, Perkins understands that modernizing a bank’s core is no longer a luxury but a survival strategy in an era where speed and reliability are the primary currencies of customer trust. In this discussion, we explore the shifting priorities of over 200 global banks that are currently racing to replace fragile legacy code with cloud-native architectures and artificial intelligence.
We dive into the practicalities of orchestrating diverse payment rails, the necessity of designing for inevitable technical failures, and the strategic importance of ISO 20022 data in building deeper consumer relationships. Perkins provides a roadmap for institutions looking to navigate the transition from COBOL-based systems to agile, AI-driven platforms that can compete with the next generation of fintech challengers.
Many financial institutions are shifting from simple back-office upgrades to full-scale modernization. How does a cloud-native architecture specifically accelerate product launches, and what are the initial steps to ensure these systems scale effectively without over-provisioning for peak demand?
A cloud-native strategy is the engine of agility because it moves us away from the rigid, “one-size-fits-all” constraints of on-premises hardware. When we talk about acceleration, we mean the ability to deploy new solutions like FedNow or RTP in a fraction of the time it would take to configure physical servers. The initial step is to embrace dynamic scalability, which allows the system to breathe with the market—expanding during high-volume periods and contracting when demand dips. This eliminates the need for expensive, idle infrastructure that banks traditionally maintained just to survive a few peak hours of traffic. By focusing on native architecture from day one, banks ensure that availability is never perceived as limited by the customer, creating a foundation where innovation isn’t slowed down by hardware procurement cycles.
Integrating artificial intelligence often goes beyond front-end features to include back-office optimization. How can banks use AI to address the technical debt of legacy code like COBOL, and what specific metrics should they track to measure the resulting improvements in operational efficiency?
AI is a transformative tool for de-risking the “black box” of legacy systems that have been customized over decades. We see immense value in using AI to understand and actually rewrite aging COBOL code, which has become a strategic bottleneck for many institutions. By leveraging these tools, banks can translate decades of “spaghetti code” into modern, documented languages that are easier to maintain and update. To measure success, banks should track the reduction in system outages and the speed of code deployment cycles. Ultimately, the goal is to see a measurable drop in the manual intervention required to keep these legacy cores running, allowing human talent to focus on product innovation rather than just keeping the lights on.
Organizations often face fragmented infrastructure when managing wire, batch, and instant payments simultaneously. What does effective orchestration logic look like in practice, and how does a unified platform maintain a seamless experience for customers as they transition between different payment rails?
Effective orchestration is about creating a common look and feel across every transaction, regardless of the underlying rail. In practice, this means if a consumer decides to switch from using traditional debit rails to FedNow for a payment tomorrow, the transition should be invisible to them. A unified platform uses intelligent orchestration logic to cost-effectively manage these historically different use cases behind a single interface. By consolidating wire, batch, and instant payments into one cohesive infrastructure, banks avoid the trap of “siloed” modernization. This allows the bank to manage risk and liquidity centrally, ensuring that the customer receives a consistent, high-quality experience whether they are sending a high-value wire or a micro-payment.
In an environment where technical failure is considered inevitable, how should systems be designed to remain operational during network outages or fraud spikes? How does the accelerated pace of real-time payments change the way risk management teams detect and prevent financial crimes?
We have to operate under the mantra that things are eventually going to go down, so we design for “always-on” resilience. This means building operational components that can continue processing transactions safely even during a network outage or a massive surge in volume. Real-time payments have dramatically narrowed the window for fraud detection, making traditional “after-the-fact” reviews obsolete. Risk management teams must now deploy AI-driven fraud controls that act as quickly as the transaction itself to prevent financial crimes before the money leaves the building. If you don’t have these highly available, real-time controls in place, you risk erodining the very customer trust that your bank is built upon.
Using ISO 20022 standards provides deeper insights into consumer behavior and transaction history. How can banks leverage this data to build more meaningful customer relationships, and what specific challenges arise when migrating from older messaging formats to these richer, more complex data sets?
ISO 20022 is a goldmine for understanding the “why” behind a payment, offering a level of data richness that older formats simply couldn’t carry. Banks can leverage this to see patterns in how and what consumers are buying, allowing them to offer hyper-personalized financial advice or products at the exact moment they are needed. However, the migration is challenging because it requires moving from simple, text-heavy messages to complex, structured data sets that old legacy cores struggle to ingest. The hurdle isn’t just technical; it’s about ensuring that every system in the payment chain can speak this new, sophisticated language without losing data integrity. Once mastered, this data allows a bank to stop being just a utility and start being a proactive partner in a customer’s life.
Non-traditional firms are increasingly seeking banking licenses to enter the lending and payment space. How can legacy institutions innovate fast enough to compete with these new players, and what structural changes are necessary to support emerging capabilities like stablecoins?
The competitive landscape is widening rapidly, with large non-traditional firms now seeking U.S. banking licenses to move directly into lending and payments. To compete, legacy institutions must adopt platforms that allow for “quick wins”—reusable patterns that deliver tangible business benefits early to build momentum. Structurally, banks need to move away from rigid, closed systems and toward open architectures that can integrate emerging capabilities like stablecoins or crypto-related services. This isn’t just a tech upgrade; it’s a strategic shift to ensure the bank can launch products at the same speed as a nimble fintech. If a bank can’t innovate quickly enough to provide these modern services, they will see their market share eroded by these new, aggressive entrants.
What is your forecast for the future of payments modernization?
The future of payments will be defined by a state of “perpetual motion” where the concept of being “caught up” no longer exists. We will see the total disappearance of friction for small businesses and consumers, where transactions simply work in the background without a second thought. My forecast is that AI will become the primary interface for payments, using ISO 20022 data to predict and execute transactions before a user even initiates them. We are moving toward a global, 24/7/365 ecosystem where the distinction between different payment types vanishes, replaced by a single, real-time flow of value. Banks that embrace this constant state of change today will be the ones that define the financial landscape of the next decade.