At the Crossroads: Navigating the Shift to AI and Cloud in Banking

As the world accelerates toward a digital-first approach, the banking industry finds itself at a defining moment. NTT DATA’s global research, "The Digital Horizon: Banking’s Shift from Mainframe to AI-Driven Cloud Infrastructure," finds a sector ready for innovation, ready for change, but still holding on to its legacy technology. For true transformation, there is a clear need – and desire - to move towards agile, innovative change powered by AI and cloud technologies.

The Paradox of progress against mainframe’s continued persistence?

Underpinning the banking digital landscape lies a contradiction. Despite nearly 90% of banking decision-makers identifying their organizations as AI and cloud trailblazers, a significant 63% remain anchored to legacy mainframe systems. 20% have not even started their transformations.  

The mainframe’s resilience within the banking sector underscores the cautious approach to legacy transformation, a reflection of the mainframe’s time-tested reliability and security, and the challenges of migrating well-established software stacks that predate modern cloud approaches.

The tipping point of innovation

Nonetheless, the scales seem to be tipping. Our research highlighted the swift uptake of Generative AI and the interest it is receiving at the executive level. A striking 91 percent of banking executives surveyed acknowledged that the momentum for Generative AI and Cloud adoption is backed by their boards. 
Additionally, 87 percent reported escalating demands from their boards and senior management to adopt novel AI technologies. This underscores the priorities and drive of those making strategic decisions in the banking industry.

The desire is driven by multiple headwinds

In today's fast-evolving banking landscape, decision-makers are constantly seeking ways to innovate, meet consumer and client demands, and maintain a competitive edge. The board and leadership teams understand that integrating AI and cloud technologies can be pivotal in driving efficiency, improving customer experience, and unlocking new revenue streams. Their deep interest and push for AI underscore their commitment to modernizing banking practices, optimizing operations, and future-proofing their institutions.

David Muñoz, Executive VP Technology and Operations at Banco Pichincha in Ecuador, explains how his bank is overcoming these hurdles: 

In a sector that is by its nature, risk-averse and cautious, it’s no surprise that mainframes have long been the backbone of the banking industry. Their operational resilience and robust security features have made them a trusted technology for decades. As recently as 2020, IBM published a business report1 that found 45 of the global top-grossing banks still prefer mainframe computers. 

However, change is coming.

NTT DATA’s latest research shows that cloud technologies combined with AI may be about to loosen the mainframe’s grip on banking. In a period marked by the ubiquity of digital transformation, the allure of cloud technologies' flexibility, scalability, and innovative capabilities – not to mention the draw of AI itself - is becoming increasingly hard to ignore.

The Tenacious Grip of Mainframes

Mainframes have long been synonymous with reliability in the banking sector. Their robust architecture has ensured the smooth handling of massive transaction volumes, underpinning the critical operations of banks for decades.

The demise of mainframes has long been forecasted, it is often dubbed as legacy tech on the brink of extinction. Yet, these robust machines continue to power core business processes for banks. Their longevity can be attributed to several factors:

• Reliability and Stability: Mainframes are renowned for their impressive uptime and resilience against failures, making them an attractive option for businesses that require high availability.
  
  
• Security: Built with robust security features, mainframes offer an unmatched defense against cyber threats, ensuring the protection of critical data and operations.
  
  
• Legacy Applications: Many legacy applications, developed over years, are so deeply embedded in organizational processes that migrating them to newer platforms poses a daunting challenge. The cost, risk, and time associated with these migrations often deter companies from moving away from mainframes.
  
  
• Performance: For large-scale transactional workloads, mainframes still offer impressive processing power that are powerful enough for the tasks they were built for, and new mainframe hardware is still being developed and released.

However, while these factors have contributed to the mainframe's impressive lifespan, technological advances and changing business needs indicate that its reign may be waning.

Cloud: The Strategic Enabler

Cloud-based infrastructure is not only an operational platform but also a strategic enabler. It offers banks the flexibility to adapt to rapidly changing regulatory environments, improve operational efficiency, and respond to changing customer expectations. It also offers direct access to the latest innovations in AI and analytics.

In the world of banking, where agility is key to staying competitive, the cloud's ability to facilitate rapid change is invaluable. According to NTT DATA's research, 89% of decision-makers view their banks as innovators. However, a significant 63% still rely on mainframe infrastructure, with 20% yet to start transitioning to cloud platforms. This paradox highlights the challenge of balancing innovation with the reliability of established systems.

Generative AI: A Catalyst for Change

The rise of AI, particularly Generative AI, is reshaping the capabilities of the banking sector. From data analysis to the creation of new digital outputs, AI is a transformative force. A staggering 63% of industry leaders confirm that Generative AI is accelerating their transition towards cloud-based applications. This AI-driven shift is not just about technological advancement but also about redefining the very essence of banking services and solutions.

But interestingly, generative AI itself may accelerate the move to cloud, with the technology having a role in recoding legacy applications, automating testing, and reducing the risk of migrating off mainframe hardware.