Banking Transform

Transform Banking Transform Core

As the world enters the cloud generation, mainframe-based banking platforms are nearing an evolutionary dead end. Slowly but surely, the bank’s entire technology ecosystem – from the programming languages, the operating systems, and the hardware, to the way their IT organizations operate – will all be in need of modernization. The situation is worsened by the shrinking talent pool needed to keep the legacy systems alive.

The scale of the problem

Decades of M&A activities are further exacerbating the challenges facing the banking technology infrastructure as disparate, incompatible back-end systems collide. These complex systems can cost a bank billions of dollars to simply maintain. On top of ballooning operating costs, banks are also facing the threat of upheaval: frequent outages due to system upgrades, limited digital banking offerings, slow response and problem resolution, etc.

Focusing on products alone only exacerbates the problem

Many global banks with deep pockets are racing to double down on their investment in technology innovation. The proliferation of fintech companies is providing them with a lifeline and fueling the advancement of banking digitalization worldwide, especially in the payments sector. However, banks are quickly realizing that their hands are tied when implementing many new Digital Banking Solutions.

Their efforts to plug in new products are crippled by legacy mainframe-based, monolithic architecture. Legacy core technology is simply not capable of delivering innovation in today’s digital society. To get to the root of the problem, banks need to get to the “core” of the banking system – the platform that underpins all of the banking applications.

The evolution of core banking systems

1. The first generation

The “first generation” of cores that emerged in and around the 1970s were built to emulate a model of banking centered around brick-and-mortar branches. These systems were monolithic in style and exclusively designed to run on expensive mainframes.

2. The second generation

In the 1980s, the emergence of new banking channels such as ATMs and call centers spurred on a “second generation” of banking systems. As consumer banking evolved to be less branch-centric, banks invested heavily in retooling the banking systems to achieve high resiliency and handle significant throughput at low latencies. However, the underlying architecture remained largely unchanged.

3. The third generation

At the turn of the century, the rapid adoption of online and mobile banking drove the arrival of “third generation” systems with a key requirement to handle 24/7 banking.

To address the 24/7 requirements, banks have changed systems to run in “stand-in” mode so that payments are buffered as the bank transitions over the end-of-day process. This approach added considerable complexity to the system as banks found themselves building a bank within a bank to handle stand-in.

To meet the changing consumer and regulatory demands, some banks developed new features and products using modern programming languages, which paved the way for banks to ditch their expensive mainframes. Banks that didn’t want to risk moving away from their mainframes began to adopt a “hollow out the core” strategy, which involves pulling the product engine, along with other key capabilities, out of the core.

The result is that banks can rely on more modern products to solve some of the shortcomings of the legacy core, however, the downside is that there is an increased operational complexity and integration challenge.

Introducing the fourth-generation

With the efforts to fix legacy systems, there is one common limitation – given that they are all inherently monolith, they can only be scaled vertically. This fundamentally inhibits the banks’ agility and ability to reduce costs.

Moving to the cloud

The next generation of core banking systems must be built using a cloud-first approach in mind.

Cloud technology enables banks to manage their resources on demand, and enhance the accessibility of customer data, while also offering the agility needed to process data in real-time. Capacity is effectively limitless in the cloud. Banks that want to take full advantage of cloud infrastructure need to adopt cloud native principles – building a core that is written in the cloud and for the cloud.

Building cloud-native systems requires a microservices architecture approach, with which an application is split into autonomous chunks called microservices that communicate via APIs.

Real-time access to data

Core systems built with streaming APIs offer banks the ability to process data in real-time using modern AI and analytics technologies, enabling them to respond to both customer and regulatory demands effectively and efficiently.

Gaining control of the product roadmap

Banks gain full ownership of their product roadmaps by separating the financial product layer from the platform layer within the core. They can update products and add new products without having to wait for changes to be made by a fintech, gaining tremendous flexibility and agility as they respond to fast-evolving customer and regulatory demands.

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