Engineering for an AI-enabled Bank with a Heart

In banking, trust has always mattered. But as AI advances and the operating environment becomes more uncertain, trust matters even more. Customers, regulators and partners are asking harder questions about the institutions they rely on. Trust has become a real differentiator, and one that banks cannot take for granted.

As AI becomes embedded across how financial services are designed, delivered and governed, the institutions that will stand apart are those that have made trust a deliberate engineering choice. At DBS, this thinking shapes how we build our systems, develop our people and govern our use of technology. We embody this in our ambition to be an AI-enabled bank with a heart, and the technology function has an important role to play.

Engineering trust by design

In a digitally enabled bank, trust is reinforced through technology resiliency. Customers may not see the systems, architecture or controls behind a banking service, but they feel the difference when services are reliable, secure and available when needed. In our experience, resiliency has to be intentionally designed across architecture, processes and the operating models that support how systems are built and run.

We anchor this on a framework we call “R.I.S.E” (Resilience, Innovation, Security and Efficiency). It gives our engineers a common language for what good looks like, and a shared set of standards that make execution more consistent and predictable across a large, complex organisation. In a bank of our scale, consistency matters. It helps teams make better decisions and deliver a more reliable experience for customers.

Resilience sits at the core of R.I.S.E for a good reason. In financial services, customers do not experience your architecture, they experience the outcome. When services work seamlessly, trust accumulates. This is why we treat resilience as something to be engineered proactively. Our “Predict, Prevent, Detect, Recover” approach helps teams build resilience into every stage of the system development lifecycle, so they can better anticipate, withstand and recover from unexpected incidents.

AI has also transformed how our engineers work, accelerating delivery, expanding test coverage and helping teams diagnose and respond to issues faster. Today, AI helps teams generate user stories and test cases, while automation significantly expands testing across systems. Tasks that once took months, such as modernising legacy code, can now be completed in weeks.

At the same time, AI introduces new risks that require new forms of risk management. As we embed AI more deeply into how we work, we continue to put clear controls and guardrails in place to ensure speed and efficiency never come at the expense of resiliency. They remain valuable but they must be built on a foundation that customers can rely on.

Keeping humans at the centre

Technology alone cannot build trust. The systems we engineer are only as reliable as the people who design, build and operate them. As AI takes on more of the manual and routine work of banking, the quality of human judgment becomes more important.

The more we automate, the more we need people who can think clearly about what AI should and should not be doing on a customer’s behalf. We will need talent not just for technical skills, but for the ability to exercise sound judgment, ask the right questions and take accountability for outcomes. While AI can help make decisions faster, the responsibility and accountability still sit with humans.

At DBS, this has meant building both deep technology and business domain expertise across our engineering teams, strengthening AI literacy at every level and reinforcing a technology risk culture where innovation and integrity are treated as complementary rather than competing priorities. We want engineers who understand not just how systems work, but how customers use them and why they matter.

Roles will also evolve as AI becomes more capable. Engineers who once spent most of their time writing code now spend more of it reviewing, evaluating and governing what AI produces, and many are well positioned to move into higher value roles as a result. The bank and our leaders are supporting this shift, and continued investment in upskilling and reskilling will be central to the future of the human-AI ecosystem.

Engineering for trust with intent

Across engineering and people, the thread is the same: trust has to be built deliberately and at every layer of how a bank operates. It is built through resilient systems, responsible use of technology and people who exercise good judgment every day. Customers who trust their bank are more likely to use more of its services. Regulators who trust an institution give it room to innovate responsibly. Partners who trust a technology function are more willing to build and grow alongside it.

That is how the technology function helps support DBS’ commitment to be an AI-enabled bank with a heart. As AI becomes more deeply embedded into financial services, the challenge for technology leaders is not simply to move faster, but to build systems people can depend on and use technology responsibly. In the end, trust remains the foundation on which everything else is built.