By Vikas Krishan, Chief Digital Business Officer and Head of UK and EMEA, Altimetrik
As technology reshapes financial services, payment providers are facing mounting pressure to move beyond legacy infrastructure. Many of the systems in operation today were developed for a very different business environment, often using programming languages and structures that are now decades old. Maintaining these systems is becoming not only more costly but also more complex, creating substantial risks for organisations seeking agility and competitiveness.
What makes this moment especially urgent is the growing expectation among customers and partners for seamless, secure and data-rich payment experiences. Legacy systems, by design, were never built to meet these demands. They are brittle, siloed and increasingly incompatible with modern frameworks. In this context, artificial intelligence is emerging as a powerful enabler of digital business. It offers the potential to modernise payment platforms without bringing operations to a halt. An AI-first approach can help payment providers overcome three critical challenges: architecture limitations, data inaccessibility and the risks tied to large-scale modernisation.
The legacy burden
Many of today’s payment systems are the result of successive mergers, acquisitions and system expansions. This often leads to what might be described as a “Frankenstein approach”, where disparate components have been stitched together over time. The result is fragile architecture that is prone to compatibility issues and operational bottlenecks. These systems are difficult to scale and resist integration with newer technologies, making innovation expensive and disruptive.
Alongside architectural rigidity is the challenge of data accessibility. Payment platforms generate vast quantities of valuable transaction data. Yet legacy environments typically lack the tools to extract, interpret or operationalise this information effectively. The process is manual, expensive and time-consuming. Data is often stored across disconnected systems, making it difficult to build a single coherent view. Without this, even the most promising AI models struggle to deliver business value.
The third and perhaps most complex challenge is modernisation itself. Institutions know they must evolve. However, the prospect of migrating massive platforms while keeping services running creates understandable hesitation. The risks include service outages, data loss and regulatory non-compliance. A single misstep in migration planning can have consequences that reverberate across markets and damage customer trust.
The role of AI in rethinking architecture
Artificial intelligence offers a compelling response to these challenges, not by proposing wholesale replacement, but by enabling gradual, intelligent transformation. A key principle in this process is establishing a Single Source of Truth for payment data. Rather than extract and reconcile information from multiple sources manually, AI systems can continuously ingest data from across the enterprise, clean and standardise it, and provide a unified layer of insight. This not only reduces operational friction but also lays the foundation for more responsive customer services.
AI also enables a more modular approach to infrastructure transformation. Instead of attempting to replace a legacy platform in one large and risky exercise, payment providers can transition functionality in phases. AI tools can be used to analyse system interdependencies and identify which elements can be decoupled and moved first. In this way, institutions can run modern and legacy systems in parallel, progressively shifting workloads while maintaining continuity.
This model of co-existence also creates a more stable environment for innovation. New features can be developed and deployed on the modern stack, while legacy systems continue to serve existing clients. Over time, as confidence grows and dependencies are reduced, full transition becomes feasible with minimal risk.
Strategic value and competitive advantage
The business case for modernisation has never been stronger. According to recent industry forecasts, global payments revenue is expected to reach approximately three trillion US dollars by 2028. The organisations best positioned to benefit are those already taking steps to modernise their platforms today. By adopting AI-first strategies, these companies are not only streamlining operations but also positioning themselves to unlock new revenue streams and deliver more meaningful customer experiences.
Modernisation also plays a growing role in risk mitigation. Outdated systems are not only harder to secure but also make it more difficult to respond to fraud patterns, regulatory changes and emerging compliance standards. AI allows for real-time monitoring of payment flows, predictive identification of anomalies and faster resolution of incidents. These capabilities are no longer optional in an era of escalating cybersecurity threats and tighter regulatory scrutiny.
A smart approach to change
One of the key concerns from payment leaders is how to modernise without disrupting existing operations. The answer lies in a pragmatic, data-driven approach. Institutions must begin by mapping their current state and understanding the full set of dependencies in their systems. From there, they can use AI tools to simulate migration pathways and stress-test outcomes. This allows them to identify low-risk starting points and build confidence gradually.
A phased model approach is most logical. Core processes such as payment authorisation or reconciliation can be moved into the new environment first, supported by AI-based monitoring. This enables teams to observe the impact and adjust before moving to more complex areas. Legacy systems are maintained during this time but with decreasing load. Once stability and reliability are demonstrated, the older components can be decommissioned cleanly.
This is not only a safer path but also a more politically viable one. It allows transformation to be championed from within, with stakeholders seeing tangible benefits early in the process. Crucially, it avoids the “big bang” approach that so often leads to delays, budget overruns and internal resistance.
Artificial intelligence is no longer an experimental technology in payments. It is a strategic asset that can support transformation at every stage, from architecture design to operational oversight. The challenge now is less about technical feasibility and more about leadership and execution. The payment institutions that succeed over the next five years will be those that embrace AI not as a one-time project, but as a foundation for continuous improvement. This requires vision, governance and a willingness to reimagine what is possible. Those that move early will be better able to respond to market shifts, capitalise on growth opportunities and build a more resilient operational core.