Priya Jaiswal is a luminary in the realm of banking, business, and finance, bringing decades of experience in market analysis, portfolio management, and international business trends. With a keen eye for spotting systemic challenges and opportunities in the fintech space, Priya has guided countless institutions through the labyrinth of technological transformation. Today, we dive into her insights on the critical issues plaguing core banking vendors and the innovative paths forward for the industry. Our conversation explores the deceptive practices of vendors, the architectural limitations of legacy platforms, and the bold moves by banks to redefine their technological foundations.
How have you seen core banking vendors market outdated platforms as cutting-edge solutions, and what has been the fallout for the banks that bought into these promises?
I’ve witnessed this far too often, where vendors pitch so-called “next-generation” platforms that are essentially last-gen systems with a shiny new API layer slapped on top. I recall a mid-sized bank I worked with a few years back that fell for this trap. They invested millions in a system touted as revolutionary, only to discover it couldn’t scale with their digital transaction volumes because the core was still rooted in outdated architecture. The impact was brutal—customer complaints skyrocketed due to sluggish processing times, and the bank’s IT team was stuck in a cycle of endless patches. Over time, they had to allocate nearly 30% of their annual IT budget just to maintain this “modern” system, draining resources from true innovation. It was heartbreaking to see their trust erode, not just in the vendor but in the idea of transformation itself. They eventually started exploring alternative providers, but the sunk cost and integration complexities made it a slow, painful shift.
Can you share a specific experience where a vendor promised a cloud-native solution but delivered something far less adaptable, and how did the bank navigate the resulting challenges?
Absolutely, there was a case with a regional bank I advised where the vendor sold them on a “cloud-native” platform, but what they got was a monolithic system merely containerized to appear modern. The mismatch became evident when the bank tried to integrate with fintech partners for real-time payment solutions—the system required dedicated infrastructure and couldn’t handle the dynamic scaling that true cloud-native systems offer. This created bottlenecks, with transaction delays during peak hours frustrating customers and costing the bank in lost business. Their workaround involved hiring a team of external consultants to build custom middleware, which added another layer of expense and complexity. I remember sitting in their boardroom, feeling the tension as they realized this wasn’t a quick fix but a multi-year burden. They did manage to stabilize operations, but it diverted focus from strategic goals like enhancing customer experience to just keeping the lights on.
What’s an example of a vendor’s licensing model or business strategy that prioritized lock-in over value, and how did it stifle a bank’s ability to innovate?
I’ve seen punitive licensing models create real havoc for banks. One instance that stands out involved a large bank that signed on with a vendor whose licensing fees escalated dramatically if they attempted to integrate third-party solutions or reduce usage of certain modules. This essentially trapped them into using the full suite, even when parts of it were redundant, costing them an extra 15-20% annually in unnecessary expenses. Operationally, they couldn’t experiment with innovative fintech tools because the cost of breaking away or even testing alternatives was prohibitive. I could sense the frustration in their innovation team—they had ideas for personalized banking services but no budget or freedom to execute. Long-term, they had to rethink their entire vendor strategy, which delayed their digital roadmap by at least two years while competitors surged ahead with more agile partnerships.
Could you walk us through a situation where a vendor’s solution promised quick integration but led to prolonged pain for a bank, and how they dealt with the aftermath?
I remember a bank that was lured by a vendor’s promise of a “quick win” integration for a new digital banking module. The pitch was all about seamless deployment within months, but it turned into a three-year nightmare of incompatible systems and data silos. The integration pain came from poorly documented APIs and a lack of support for existing infrastructure, forcing the bank to overhaul much of their internal tech just to make it work. The cost overruns were staggering, nearly doubling their initial budget, and the delays meant they missed key market windows for launching new products. I was in meetings where the IT head described feeling like they were drowning in technical debt before even going live. They coped by bringing in a specialized integration firm, but it was a Band-Aid on a gaping wound, and trust in external solutions took a massive hit.
How have you seen banks get trapped in dependency on vendors due to overly complex platforms, and what strategies did they use to manage that relationship?
There was a case with a community bank that adopted a core banking platform so intricate that it required an army of consultants just to keep it running. The vendor had designed the system with such proprietary complexity that even basic updates needed their direct involvement, costing the bank upwards of $500,000 annually in consulting fees alone. This dependency made switching unthinkable—migration would have been like rebuilding their entire tech stack from scratch. I remember the palpable anxiety in their leadership team as they realized they were married to this vendor, not by choice but by necessity. To manage, they negotiated long-term support contracts to cap costs, but it was a grudging compromise. They also started training internal staff to reduce reliance, though progress was slow given the system’s steep learning curve. It was a stark lesson in how vendor design can turn a partnership into a prison.
Can you describe a time when a bank struggled with a platform’s inability to support AI-driven needs due to outdated architecture, and how they attempted to adapt?
I worked with a bank eager to roll out AI-driven customer insights, but their core platform was built on a batch-processing architecture that couldn’t handle real-time data demands. Transactions processed overnight meant their AI models were always a day behind, rendering predictive analytics for fraud detection or personalized offers nearly useless. The technical gap was glaring—there was no way to ingest streaming data or enable real-time decisioning without a complete overhaul. I could feel the disappointment in the room when their data scientists presented mock-ups that couldn’t be operationalized. Their pivot involved layering a separate real-time processing tool on top, which was costly and only partially bridged the gap. They’re still grappling with fragmented systems, a constant reminder of how yesterday’s architecture can sabotage tomorrow’s vision. It pushed them to start planning for a full core replacement, though the migration risk looms large.
Have you encountered a vendor overhype AI capabilities in their platform, and what was the impact on the bank’s expectations and outcomes?
Oh, definitely. I recall a vendor pitching an AI-powered decisioning tool to a bank I advised, claiming it would revolutionize customer interactions with real-time insights. The reality was a far cry from the hype—the “AI” was just basic rules-based automation with no machine learning depth, and it required multiple API calls for even simple tasks, slowing everything down. The bank had budgeted for a game-changer, expecting a 20% uptick in customer engagement metrics, but saw no measurable improvement after deployment. I remember the CIO’s frustration boiling over in a review meeting, feeling duped after such lofty promises. Trust in that vendor eroded completely, and they scrapped plans for further modules, opting instead to pilot smaller, more transparent AI solutions from startups. It was a costly lesson in vetting buzzwords against actual capabilities.
Could you tell us about a bank that took the bold step of building its own core platform instead of relying on vendor solutions, and what were the key drivers and initial outcomes?
I’ve seen an innovative mid-tier bank take the audacious step of building their own core platform from the ground up, driven by sheer frustration with vendor lock-in and outdated tech. They’d been burned by a legacy system that couldn’t support their mobile-first strategy, and with markets moving in weeks rather than years, they couldn’t wait for vendor roadmaps to catch up. The decision was fueled by a visionary CTO who believed in-house control was the only way to ensure agility. Early challenges were immense—hiring specialized talent and securing board buy-in for a multi-million-dollar project took over a year. But initial successes were promising; within 18 months, they rolled out a composable architecture that slashed integration times for new fintech partnerships by half. I was inspired by their grit, though the journey was far from over. It showed me what’s possible when banks prioritize future-proofing over short-term fixes.
What’s your vision for a core banking platform truly designed for intelligence rather than just transactions, and how would it transform daily operations compared to current systems?
My vision for an intelligence-focused platform starts with a foundation built for real-time, AI-native operations, not bolted-on features. Step one is a data model treating information as fuel for AI, not just records in a database, enabling continuous learning from every interaction. Step two is true composability—microservices that let banks plug in best-of-breed tools without integration headaches. Step three is prioritizing continuous deployment over quarterly releases, ensuring updates roll out in days, not months. Unlike today’s platforms, where overnight batch processing delays decisions, this system would enable real-time insights, so a customer’s loan approval or fraud alert happens instantly at the teller window. Operationally, it transforms everything—imagine tellers and apps powered by a single brain, not fragmented modules, reducing errors and wait times. I picture bank staff feeling empowered, not bogged down by clunky systems, and customers sensing that seamless efficiency. It’s a platform banks build on, not get trapped in, fostering innovation at every touchpoint.
Looking ahead, what is your forecast for the future of core banking platforms as AI and real-time needs continue to reshape the industry?
I believe we’re at a tipping point where core banking platforms will either evolve into AI-native, composable systems or become relics that hold banks back from the future. Over the next five to ten years, I foresee a split—vendors who cling to legacy architectures will lose ground to new entrants and in-house solutions designed for intelligence from the start. The demand for real-time decisioning and personalized services will force platforms to prioritize data fluidity and scalability, or risk obsolescence. I’m optimistic that pressure from innovative banks building their own cores will push the industry to rethink value over lock-in, though it’ll be a rocky transition with plenty of migration pain. My hope is that we’ll see platforms become true enablers of transformation, not anchors to the past, but it’ll take courage from vendors to rebuild rather than repackage. I’m curious to see how quickly this shift happens—will it be a gradual evolution, or a sudden disruption driven by a game-changing newcomer?
