The core banking software industry is at a pivotal moment, with increasing pressure from both technological advancements and evolving customer demands. In 2017, Jon Webster, a banking technology expert, envisioned the creation of a "Kubernetes of Banking" — a transformative platform that would be so compelling and cost-effective that banks would be forced to migrate to it from their outdated core systems. Nearly a decade later, that vision remains unrealized, and the viability of the core banking software industry is being questioned.
Despite the emergence of new technologies, no vendor, whether incumbent or newcomer, has managed to create the Kubernetes of Banking. The arrival of artificial intelligence (AI) has added complexity to the issue, reducing the barriers for banks to modernize and introducing the possibility of AI agents eventually replacing traditional banking software. As switching costs drop and AI becomes more integrated into banking systems, core banking vendors are facing a potential existential threat. With an addressable market expected to grow steadily, the question remains: how long can core banking software vendors rely on their installed base and traditional market dynamics?
Over the past decade, the core banking software industry has undergone a seismic shift. Once dominated by a few large, incumbent vendors, the market is now a complex web of players, including cloud-native vendors, banking-as-a-service (BaaS) providers, and challenger banks monetizing their technology platforms. The key competitive advantages for incumbents remain comprehensive functionality and proven success with legacy migrations, but newer entrants have set new standards with their agility and cost-effectiveness. These neo-vendors offer fast implementation timelines, flexibility, and a "try before you buy" approach, making digital transformation more accessible to banks.
While these new players offer promising features, they have yet to demonstrate the ability to scale or handle large-scale migrations from legacy systems. To achieve the market dominance required to create the Kubernetes of Banking, both traditional and new vendors need significant investment. Incumbents must reinvent their product and sales structures to meet the technology demands of the modern banking landscape, while neo-vendors need time and resources to build the functionality required for large-scale deployments.
A key challenge facing core banking vendors is the pressure to build rather than buy. AI has the potential to reduce banks' reliance on third-party vendors by enabling in-house development teams to create their own solutions. AI could assist in writing code, automating legacy system conversions, and even generating synthetic testing data. However, AI’s capabilities are still limited to specific tasks, and most banks, except for the largest institutions, do not have the data necessary to train AI models on the scale needed for banking solutions. As a result, the addressable market for third-party banking software remains secure for the foreseeable future.
Microsoft CEO Satya Nadella recently predicted that AI-powered agents could replace traditional SaaS and business applications, with business logic shifting into an AI layer. While this may eventually transform other industries, banking presents unique challenges due to its complexity, regulation, and fragmented workflows. The current state of banking—still largely reliant on traditional core systems—may not yet be ready for a fully AI-driven future. Instead, a modernized core banking platform that integrates AI and offers real-time transaction processing may be the key to unlocking this next phase.
Historically, core banking has evolved rather than been replaced by disruptive technologies. The move to cloud-native systems, the integration of blockchain in payments and trade finance, and the adoption of microservices have all reshaped the industry. The Goldilocks principle—breaking down banking capabilities into manageable domains—has proven to be the right solution for most banks, allowing them to scale while maintaining future operability.
AI will certainly drive the next evolution of banking software, but it is unlikely to be the death knell for core banking systems. Banking technologists are adept at embracing new technologies without succumbing to the hype. The most likely scenario is that AI will be embedded into core banking software, making it more intelligent and autonomous, thereby delivering tangible business value to banks. The first vendor to successfully navigate this evolution, with a clear long-term vision and a commitment to innovation, could ultimately fulfill the promise of the Kubernetes of Banking.
