AI is already disrupting every area of the financial services industry, and is being included in almost every strategic conversation around technology-enabled transformation.
This transformation is exemplified by industry leaders like JP Morgan Chase, where CEO Jamie Dimon has championed a £12billion annual investment in data and technology overseeing over 400 AI use cases including fraud detection, customer service improvements and operational efficiencies across the bank.
The core platforms that underpin the industry together risk buckling under the weight of modernisation, writes Vikas Krishan, chief digital business officer and head of EMEA at digital enablement business Altimetrik.
AI is gradually loosening the components of legacy institutions and presenting fresh opportunities that are scalable, resilient and adaptable to the agile needs of financial services.
Through this reimagining of core platforms, those who choose to act now can expect to leapfrog their competition, while those who fail to act now risk obscurity, lack of productivity and being disregarded by their consumer base.
The transition to new architectures
For decades, banks have relied on legacy systems to power their core operations. These often aging platforms are becoming increasingly difficult and expensive to maintain, having been built both in languages not commonly used and architected with a different business reality in mind.
They frequently lack the flexibility required to meet the demands of today’s digital-first customers and struggle to integrate with modern financial technologies. A significant challenge facing organisations is the accumulation of technical debt; the cost of additional work or rework caused by choosing quick or limited solutions over more robust, maintainable approaches. Over time, this can lead to significant issues that compound the challenges of legacy systems.
This lack of nimbleness is often the by-product of a Frankenstein approach to architectural systems. Many financial institutions have traditionally, when going through a merger or acquisition, built new features or attempted to fuse together two platforms. This is a very delicate balancing act, requiring extensive planning and careful execution.
If done with limited oversight, challenges can arise, including operational disruptions, increased security risks and obvious incompatibility issues. The high risks and cost burdens associated with maintaining legacy platforms has led many banks to reconsider traditional merger approaches, increasingly opting for modern, cloud-based microservices driven solutions that offer enhanced scalability, security and integration potential.
The way forward
As the industry establishes governance around this necessary transition, core platforms are being replaced by newer, more adaptable microservice-based architectures. Navigating this evolution requires leveraging an industry partner with a deep understanding of the complexities as well as the risks involved in moving from monolithic core systems to flexible, modern frameworks.
If we think back five years or so, many players in the market were already aware of this critical shift. Companies like Misys and Avaloq were acquired by private equity firms and given substantial investment to advance digital initiatives, developing solution suites. The reason for this was clear, everyone understood the market was changing.
However, the challenge still remains in managing the migration of large, complex platforms. The key question has always been how to de-risk these migrations when moving to newer architectures. This is an issue across organisations, and it is something that we at Altimetrik actively work with clients in financial services to address.
Data first
If we consider platforms such as core banking or payments systems, fundamental tools we use every day; the data generated from these transactions should, in theory, hold value. However, gaining insights from legacy platforms is significantly more challenging and the cost of extracting and utilising that data is often prohibitive. It is here that a data-driven approach must be agreed upon.
High-quality, accurate data lies at the core of every successful AI implementation. AI thrives on data; the more precise the data, the better the AI can learn and provide reliable insights. This fundamental truth highlights the importance of data integrity within the AI ecosystem. However, many financial institutions are struggling in this area, both in effectively using internal data and leveraging accurate, timely external data. As companies grow, their data environments become increasingly complex, adding to these challenges.
As financial services organisations expand, they often face the challenge of data silos, declining data quality and scattered, disconnected data repositories. This leads to a fragmented data ecosystem, limiting AI’s potential to deliver meaningful insights and drive improvements. This transformation requires active leadership from the top. Successful digital transformation depends on executive-level commitment and understanding.
Leaders like Brian Moynihan of Bank of America and Charles Scharf of Wells Fargo demonstrate how CEO ownership of data and AI initiatives drives organisation-wide adoption and success. Their hands-on approach ensures these technologies aren’t just IT projects, but core business strategy enablers.
Moving forward
To overcome this, financial institutions should establish a single source of truth (SSOT) and in doing so move away from older, somewhat clumsy core platforms. An SSOT will provide a unified, consistent view of data across the organisation.
This accelerates decision-making with greater confidence, as demonstrated by successful implementations across the industry, from the Bank of America’s AI-powered virtual assistant Erica providing personalised financial advice to Wells Fargo’s modernised data infrastructure enabling enhanced risk assessment and management. By centralising core data, an SSOT enables the identification of operational inefficiencies, better monitoring of customer behaviors and effective execution of strategies to foster growth.
The key question that comes to mind is how to successfully de-risk this transition from a fixed cost base to a more flexible, agile one. This transition is essential for becoming an outcomes-focused business with greater adaptability, but how can technology help achieve this?
One approach involves what is often (unfortunately) referred to as a ‘strangler pattern’. Instead of a wholesale shift from one platform to another, this modulated approach guides clients on a journey that focuses on gradually moving specific functionalities.
Digital businesses
By decomposing the legacy system function by function, we rebuild each component within the new platform. This allows the old system to run in parallel until fully replaced, shrinking the monolithic structure in a manageable, low-risk way. It is a method preferred by many large financial services players when they move to become digital businesses.
By working within a digital business methodology that prioritises outcomes over technology, we gain significant advantages. The beauty of this function is its flexibility. When implementing a new function, in doing so the management of a financial services firm may discover it isn’t meeting expectations or fulfilling business needs.
And yet these clients still have the security of the old platform to fall back on and can easily revert back to the original system and refine the new function before trying again. This way of working ensures a safety net, reducing risk and enabling iterative improvements without causing major disruptions to business operations.
The full picture
The transformation of core platforms through AI presents both immense opportunity and significant challenges. Those institutions willing to embrace this change, adopting data-first approaches and modern architectures, are poised to redefine the industry landscape.
The transition, whilst complex, can be managed through measured strategies like the Strangler Pattern, allowing for gradual, low-risk modernisation. As we move forward, the success of financial institutions will increasingly hinge on their ability to harness AI‘s potential, create unified data ecosystems and adapt to the evolving needs of the digital age.
Financial services businesses must embrace AI and modernise their core platforms or risk becoming as obsolete as a floppy disk.
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