Vijay Raina is a seasoned expert in enterprise SaaS technology and software architecture, specializing in the digital evolution of the financial sector. With extensive experience in guiding institutions through complex migrations, he focuses on how banks can shed the weight of legacy systems to embrace cloud-native agility. In this conversation, we explore the shift from “adapting” core software to “adopting” standardized platforms, the critical role of clean data for AI, and the strategies necessary to thrive in a market where technology and business strategy must move in perfect lockstep.
Nearly a third of legacy banking applications are currently undocumented. How does this lack of transparency increase operational risk during a digital transition, and what specific steps should a leadership team take to audit these “hidden” systems before migrating to the cloud?
The fact that 28% of legacy applications are undocumented is a staggering reality that creates a “black box” effect within a bank’s infrastructure. When you don’t know how a system was built or how it interacts with other layers, every update feels like pulling a thread that might unravel the entire fabric of the institution. To mitigate this risk, leadership must initiate a rigorous discovery phase that maps out every dependency and data flow, treating the audit not just as a technical chore but as a strategic de-risking exercise. By shedding light on these hidden systems, teams can avoid the “lift and shift” traps that often lead to catastrophic outages or security vulnerabilities during cloud migration.
Banking institutions typically invest between 6% and 12% of their revenue into IT, yet much of this is consumed by technical debt. Why does patchwork customization lead to such high maintenance costs, and how can a “standardized” model help reallocate those funds toward innovation?
Patchwork customization is essentially a form of high-interest debt; every time a bank hardcodes a unique solution for a specific customer demand, they add a layer of complexity that must be manually maintained during every subsequent update. This results in an environment where a massive portion of that 6% to 12% IT spend is wasted just keeping the lights on, rather than building new features. Shifting to a standardized SaaS model changes the dynamic because the vendor takes on the burden of maintaining and evolving the core. This “adopt” strategy frees up significant capital and engineering hours, allowing the bank to stop acting as a software maintenance house and start focusing on the “last mile” of value creation.
Integrating generative and agentic AI requires clean data and real-time integration. How do fragmented legacy environments specifically hinder these advanced capabilities, and why is a cloud-native core necessary to surface the predictive insights needed for hyper-personalized customer offerings?
You cannot build a sophisticated AI strategy on top of a fragmented foundation because AI is only as good as the data it feeds on. Legacy environments often trap data in silos or use core software that hasn’t been refreshed in over a decade, making real-time integration nearly impossible. A cloud-native core acts as a unified engine that provides the clean data and consistent processes required to embed generative and agentic AI directly into the banking workflow. This architectural shift is what allows a bank to move from generic services to hyper-personalized offerings, where the system can actually anticipate a customer’s needs rather than just reacting to their requests.
Many institutions are moving away from wholesale core replacements in favor of progressive modernization. What are the practical advantages of retiring customizations one by one, and how does this approach ensure business continuity while rebuilding around flexible, cloud-native services?
The “big bang” approach to core replacement is increasingly seen as too risky, leading many banks to favor a progressive modernization strategy that emphasizes resilience. By retiring customizations one by one, a bank can slowly simplify its integration layers and replace them with standardized services without disrupting the daily banking operations of millions of customers. This modular transition allows the institution to test and validate each new cloud-native service in a live environment, ensuring that the transition to a more agile architecture is both stable and measurable. It’s a pragmatic way to rebuild the plane while it’s in the air, focusing on reducing technical debt at a manageable pace.
The average retail customer currently holds roughly 2.59 products with their bank. How can a unified SaaS platform help deepen these relationships through behavioral data, and what specific “last mile” digital experiences should banks prioritize to remain competitive against digital-only fintechs?
When a customer only holds 2.59 products, there is a massive untapped opportunity to grow that relationship, but it requires a level of intuition that legacy systems simply cannot provide. A unified SaaS platform provides a single, consistent view of the customer, allowing the bank to use behavioral data to offer things like personalized investment recommendations exactly when a life event occurs. Banks must prioritize the “last mile” experiences—the intuitive, digital-first interactions that happen at the point of need—to match the agility of fintechs. This means using predictive insights to surface the right product at the right time, turning a static banking relationship into a proactive financial partnership.
Shifting to a SaaS model involves a transition from “adapting” to “adopting” core software. How do you distinguish between high-value configuration and the heavy customization that hampers long-term agility, and what is the best strategy for maintaining a disciplined architecture over time?
The distinction lies in where the code lives; high-value configuration uses the platform’s built-in tools to tailor the experience, whereas heavy customization involves hardcoding changes that break the upgrade path. “Adopting” means you accept the core’s standard functionality and focus your energy on the “last mile” of the customer experience where true differentiation happens. To maintain a disciplined architecture, banks must move away from the habit of customizing by default and instead prioritize simplification. This requires a cultural shift where the goal is to keep the core clean, allowing the institution to take advantage of managed updates and scale with confidence without becoming buried in complexity again.
What is your forecast for SaaS in core banking?
I believe SaaS will soon cease to be seen as a mere “deployment choice” and will instead become the mandatory standard for any bank that wishes to remain relevant in a market defined by rapid innovation. Within the next five years, the divide between banks that have “adopted” standardized, cloud-native cores and those still “adapting” legacy systems will become insurmountable. We will see a wave of consolidation where institutions with agile, SaaS-driven foundations can launch products in weeks rather than years, effectively outcompeting anyone still tethered to undocumented code and high technical debt. The future of banking isn’t just about moving to the cloud; it’s about building a purposeful, disciplined architecture that allows for intelligent, real-time decision-making at a global scale.
