Tom Fairbairn, distinguished engineer at Solace
The digital banking revolution, previously defined as the shift from bricks-and-mortar branches to apps and online platforms, is entering a more complex chapter. Artificial Intelligence (AI), a once-fringe tool reserved for automating back-end tasks, is now at the very heart of financial services.
From real-time fraud detection and trader’s risk analysis to customer engagement and hyper-personalised experiences, AI is reshaping banking.
This begs some critical questions. Are traditionally conservative financial institutions truly ready for the infrastructure demands of AI-first operations? Can they deliver consistent levels of customer service as technology outpaces the systems they’re built on? Will they be able to keep highly-sensitive data safe in a world where banking regulations are forcing banks to rethink which countries they want to operate in?
From periphery to core
Week by week, the number of physical UK bank branches diminishes. Since 2014, there has been a net decline of almost 50% in bank retail units despite online banking continuing to move to the forefront of financial services.
The shift to digital channels while raising both convenience and efficiency, raises urgent questions about the future of customer service. AI offers tailored assistance and round-the-clock availability but risks making customer experiences feel impersonal or transactional.
But the genie is out of the bottle. AI’s growing influence in banking is no longer speculative. It’s already central to how institutions operate. Machine learning models are analysing millions of data points to detect fraudulent activities faster than any human team could. Predictive analytics guide loan approvals, investment decisions, and credit scoring with more precision. Virtual assistants and chatbots are handling customer queries at scale.
The potential of AI goes far beyond operational efficiency. It holds the promise of services innovation, delivering hyper-personalised financial services, customised advice, real-time insights, and intelligent automation tailored to individual behaviour.
Thanks to the hype surrounding AI, this is the future that customers are increasingly coming to expect. Turning that promise into reality requires more than algorithms; it demands robust, agile, and scalable infrastructure.
Legacy infrastructure meets its limits
The challenge in many banks, particularly traditional incumbents, is their continued reliance on legacy systems. These systems, built for a different era of banking, often lack the flexibility to integrate new technologies at scale.
So when AI applications attempt to interface with outdated infrastructure, the results are bottlenecks, delays, and, perhaps most damaging downtime and all lead to negative customer experiences.
Customer experience is the new battleground
The ever-growing number of transactions online or via mobile apps, adds pressure to this infrastructure. The rise of mobile-first challenger banks has further raised the bar for user expectations. Consumers now demand instant, 24/7 access to their financial services. Any delay, downtime, or glitch is not just inconvenient, it’s a dealbreaker.
Powering real-time responsiveness
One of the most promising solutions to this infrastructure dilemma lies in embracing event-driven architecture. Unlike traditional models that rely on continual polling and batch processing, event-driven systems operate in real time, reacting to specific triggers, or “events”, such as a customer initiating a payment or logging into their account.
This approach allows data to flow dynamically and efficiently between systems, reducing latency and ensuring transactions are processed without delay. By only acting when necessary, banks significantly reduce resource usage while delivering a smoother and faster user experience. Importantly, it also enables better scaling under high transaction volumes, something that’s becoming increasingly vital.
Future-Proofing financial services
For AI to function optimally, especially in customer-facing applications, this level of responsiveness is crucial. Real-time data streams are what feed intelligent systems the insights they need to adapt to customer behaviour, flag anomalies, and generate personalised recommendations on the fly.
Cloud-native platforms, API-led integrations, and event-driven models are no longer optional extras – they are the new baseline for competitiveness in the AI era. Banks must also consider the importance of transparency, data governance, and ethical AI to build trust and maintain compliance in an increasingly regulated digital world.
As we move deeper into the AI-first era, banks face a choice. They can try to retrofit outdated systems and risk falling behind, or they can embrace this evolution and rearchitect their infrastructure for what’s next. The digital banking revolution may no longer be new, but it is far from over. It’s simply evolving, and for those ready to invest in the right foundations, the opportunity is greater than ever.
