What digital transformation has already delivered
Three concrete wins are broadly in place across the sector.
Most retail transactions now happen in digital channels. Branch footprint has fallen, and the branches that remain have evolved into advisory or servicing hubs rather than transaction processors. Customer expectations around speed, availability, and self-service have been reset permanently.
Cloud infrastructure has moved from permitted to preferred. Regulators, including the OCC, PRA, EBA, and APRA, have clarified their expectations. The default pattern for new workloads in banking is now cloud-native, with multi-region resilience and customer-managed keys for sensitive data.
API-first architecture is established inside progressive banks. Product capabilities are exposed through internal and external APIs. This unlocks faster product iteration, cleaner partnerships, and the ability to participate in embedded finance distribution.
These wins are real. They are also the table stakes for the next phase, not the finish line.
Where the remaining value actually lives
Five investment areas separate the banks that are pulling ahead from those that have plateaued after the first wave of digitisation.
Core modernisation
Most mid-market and large banks still run their systems of record on cores that were built for a different era. Product changes take months. Regulatory reporting is a quarterly scramble. Real-time payments, AI personalisation, and open finance all collide with batch-first core logic. The banks that are moving fastest have committed to either a full migration to a modern core (Thought Machine, Mambu, 10x Banking, Tuum) or to a disciplined strangler-pattern wrap that exposes the legacy core through modern APIs while new products run on the new stack. Doing neither is the slow-moving disaster.
Data platform consolidation
Every high-value capability downstream of a bank depends on clean, unified, well-governed data. Customer 360, real-time fraud, AI personalisation, regulatory reporting, and executive analytics all share the same substrate. Banks that have invested in a single event stream, a governed feature store, and a clear domain ownership model ship new capabilities in weeks. Banks that still copy data between point systems ship in quarters and apologise to auditors more than they would like.
AI and generative AI delivery
AI has moved from lab to production. The leading use cases, customer service case deflection, fraud investigation, compliance drafting, and engineering productivity, are now delivering measurable P&L impact. What separates the banks that are capturing value from those that are running stalled pilots is less about the models and more about the operating model: a clear AI governance framework, a reusable platform with observability and evaluation baked in, and product teams that treat prompts and tools as first-class software artefacts.
Operational resilience
With DORA in force across the EU since January 2025 and equivalent expectations rising from US and APAC regulators, operational resilience has moved up the boardroom agenda. This is not a tooling purchase. It is a programme that touches vendor management, incident response, chaos testing, exit planning for critical third parties, and evidence of the controls under stress. Banks that can demonstrate this will find their regulatory relationships smoother. Banks that cannot will face tighter supervisory attention.
Customer experience beyond the app
The first generation of bank mobile apps focused on self-service parity with the branch. The next generation is proactive: surfacing savings opportunities, flagging cash flow risk before overdraft, renegotiating recurring expenses, and offering contextual guidance in the moments that matter. This requires the data platform, the AI capability, and the API depth to compose the experience, which is why it usually lags core modernisation by a cycle or two.
Why digital programmes stall
Five patterns show up repeatedly in bank digital programmes that have plateaued.
- Underinvestment in the platform layer. Banks that fund feature delivery but not the shared platform that features depend on end up with a patchwork of independent builds, each with its own observability, deployment pattern, and compliance posture. Total cost of ownership balloons. Velocity falls.
- Transformation as a side project. A digital transformation that runs parallel to the operational bank, with separate budgets and unclear decision rights, rarely changes the mother ship. The transformation has to be inside the operating model, not beside it.
- Vendor-led roadmaps. Banks that hand strategy to a single systems integrator or a single core vendor often end up with the vendor's business case, not their own. Roadmap ownership matters.
- Talent strategy mismatch. Modernisation demands engineering leadership with modern skill sets. Banks that still recruit against two-decade-old JDs, or that underinvest in in-house capability, remain structurally dependent on consultancies that do not have incentive to finish the work.
- Governance inertia. Modernisation touches risk, compliance, audit, security, and architecture review boards simultaneously. Programmes without executive sponsorship, clear escalation paths, and pre-agreed governance tradeoffs get ground down by the very processes they are trying to modernise.
A five-part investment frame for 2026 and beyond
For a CTO building next year's plan, the following allocation has proven durable across the banks Scrums.com works with.
- Maintain and run. The irreducible cost of keeping the existing estate safe, compliant, and resilient. Scrutinise it but do not starve it.
- Core and data platform investment. The single largest multi-year bet most banks need to make. Treat it as strategic infrastructure, not a project.
- AI and automation. A steady, growing line item, funded not as a campaign but as a platform capability that every product team builds on.
- Customer-facing product innovation. The visible top of the iceberg. Fund it to win customer preference, but recognise that it depends on the layers below.
- Talent and partnership model. The cost of the people who deliver all of the above. In-house leadership, specialist engineering partners for capacity and domain depth, and a deliberate succession plan for critical roles.
Where Scrums.com fits in
Scrums.com partners with banks, credit unions, and FinTech platforms to design and deliver the software that defines modern financial services. Our banking software development services span core modernisation, data platform engineering, AI feature delivery, API platforms, and customer experience builds, all engineered for the security, resilience, and regulatory expectations of the sector.
If your digital programme needs a partner with engineering depth and regulated experience, start a project with us.
FAQ
What does "digital transformation" mean in banking in 2026?
It means the shift from a branch-led, batch-processed operating model to a software-led, real-time, API-first operating model. The early wins (mobile apps, cloud migration, basic open banking compliance) are broadly done. The next phase is core modernisation, data platform consolidation, AI delivery, operational resilience, and proactive customer experiences.
What is the biggest risk to a bank's digital programme stalling?
Underinvestment in the platform layer. Banks that fund visible features but not the shared data and engineering infrastructure those features depend on end up with rising cost of ownership and slowing velocity.
Is core banking migration worth the risk?
For many banks, yes, but not always in a single big-bang move. A disciplined strangler pattern that wraps the legacy core with modern APIs while new products run on a modern core often delivers most of the benefit with manageable execution risk.
How does DORA affect bank IT investment?
DORA, in force across the EU since January 2025, sets concrete requirements for ICT risk management, incident reporting, resilience testing, and third-party oversight. Banks subject to DORA need evidence-ready controls and exit testing for critical vendors, which has reshaped both procurement and engineering architecture decisions.
What is the right mix of in-house and external engineering for a bank?
Strong in-house leadership is non-negotiable. Specialist partners add capacity, velocity, and domain depth, especially for time-bound programmes or niche capabilities like core migration, AI platform delivery, or regulated-grade mobile builds. The worst outcome is full dependency on a single external party with no succession plan.
