Dr Antoni Vidiella, CSO of Financial Services AI Studio at Globant.
Artificial Intelligence is reshaping financial services at an extraordinary speed. Yet as banks prepare for a new wave of AI investment heading into 2026, one fact stands out: before the sector can capture the full value of AI, it must first modernise the systems at its core. Modernisation is not a side project or a short-term fix, and it has to be the prerequisite for real, measurable impact from AI.
The weight of legacy systems
Like many other industries, financial institutions continue to rely on architectures built decades ago. These legacy systems support vital functions such as risk modelling and payment processing, but have become increasingly rigid and expensive to maintain, limiting banks’ capacity to scale, innovate, and compete in a digital-first world.
Some banks are already beginning to take action on modernisation, using AI to simplify complex code and accelerate development. Recent projects show that AI-assisted code migration can reduce modernisation timelines from years to weeks, while significantly improving accuracy, documentation quality, and testing efficiency. The shift is no longer theoretical; it is measurable. In one recent engagement, a legacy COBOL platform of over 11,000 lines of code was converted into modern Java-based microservices in just 105 hours, a task that would traditionally take many months.
These results illustrate the magnitude of opportunity from applying AI to core transformation, beyond customer-facing tools. Modernisation is not about replacing old systems with new ones; it is about unlocking the agility, intelligence, and adaptability that banks need to compete.
Creating the foundation for scalable AI
AI offers huge potential for financial institutions, from improving fraud detection to enabling real-time decision-making and delivering personalised customer experiences. Yet this potential can only be realised on strong and reliable data foundations. When information is fragmented or trapped within outdated systems, AI models cannot perform accurately or transparently. The challenge becomes clear when we examine specific use cases. Fraud detection illustrates this well.
When transaction data is spread across siloed systems – some in the cloud, others on decades-old mainframes – the model cannot identify complete behavioural patterns or trigger timely alerts.
Modernisation enables banks to rebuild their technology stacks as modular, cloud-based environments that support secure, compliant, and interconnected operations. Clean, well-governed data provides the foundation for AI systems that are explainable, auditable, and aligned with evolving regulatory standards. The performance gains can be substantial. Early implementations show that AI-assisted modernisation can shorten development timelines by more than 80% while enhancing code quality and testing coverage, a level of efficiency that legacy manual approaches cannot match.
By investing in these foundations, financial institutions can ensure that innovation and trust advance in tandem, paving the way for AI that delivers lasting value to customers, shareholders, and the wider financial ecosystem.
Improving customer experience from the core
These technical foundations directly influence how banks serve their customers, defining the kind of seamless, intelligent experiences people now expect from modern financial services.
While customer experience is often associated with sleek apps or digital interfaces, true transformation happens behind the scenes. Every seamless transaction, instant credit approval, or timely account alert relies on the resilience and intelligence of the systems that power them. This is where the contrast between legacy and modern infrastructure is most apparent. Consider a simple example: applying for a mortgage. In a legacy environment, a single application may pass through multiple disconnected systems, causing manual handoffs, repeated data entry, and very long processing times. By contrast, a modernised, AI-enabled infrastructure delivers a seamless, end-to-end experience. Credit checks run instantly, risk models evaluate scenarios in real time, and personalised product options surface within seconds. The shift is not only about speed, it is also about elevating decision-making and delivering smarter, tailored outcomes at scale.
Modernising the core allows banks to unlock this level of transformation. With integrated data and connected systems, AI can enhance every stage of the customer journey, from proactive service and personalised advice to automated support and real-time engagement. This approach improves not only speed and convenience but also strengthens the trust and loyalty that define lasting customer relationships.
Modernisation strengthens security and compliance
In financial services, innovation can only thrive when it rests on a secure and compliant foundation. Modernisation enables banks to design systems where protection, oversight, and resilience are embedded from the start, rather than bolted on as afterthoughts.
Modern, transparent architectures give institutions greater visibility across their operations, allowing them to detect and respond to potential threats more quickly. This proactive approach is increasingly essential as new regulations, including the Digital Operational Resilience Act (DORA) and the EU AI Act, raise expectations around accountability and the responsible use of AI. Legacy systems, with their opaque codebases, fragmented data, and undocumented logic, make compliance more difficult and costly. By modernising their core infrastructure, banks create a turn for security and compliance into enablers of innovation, rather than constraints. The outcome is a future-ready environment where trust is reinforced, regulatory expectations are met with confidence, and innovation scales safely.
From infrastructure to innovation
Modernisation is often viewed as a technical upgrade, but in truth, it represents a strategic investment in future competitiveness. By moving beyond the constraints of legacy systems, banks gain the agility to collaborate more effectively, seize emerging market opportunities, and deliver new products and services with greater speed and precision.
Crucially, this transformation does not diminish the role of human expertise, but rather elevates it. AI will not replace the judgment, relationship-building, or strategic insight that define successful financial services.
Instead, when built on modern, secure infrastructure, AI becomes a powerful enabler. It frees people from repetitive, manual tasks and equips them with better data, deeper insights, and greater capacity to focus on innovation and customer value. Banks that invest in AI-driven modernisation today are not merely improving efficiency; they are shaping a more agile and intelligent financial ecosystem, where technology and human expertise work side by side to deliver stronger outcomes for customers, employees, and the market alike, and sets the stage for true learning organizations, with a flexible strategy that continuously adapts and optimises operations through AI feedback loops.