DBS has built foundational capabilities across data, technology, processes, and talent, while developing ADA, its enterprise data platform.
Key takeaways
DBS Bank has invested over a decade in AI, recognising its potential to transform operations and decision-making.
AI is viewed as a core business capability since 2018, requiring organisational change and governance.
DBS Bank has spent more than a decade investing in data, analytics and artificial intelligence, but the financial institution says its AI transformation has been driven as much by governance, operational discipline, and organisational change as by the underlying technology itself.
In an interaction with AIM, Saurabh Mittal, Head of Transformation and Data at DBS Bank India, says the bank began viewing AI as a core business capability around 2018, when it recognised the technology's potential to transform decision-making, customer engagement, and operations at scale.
"AI is not new to DBS. We have been investing in data, analytics, and AI for well over a decade," Mittal remarks. "We recognised that to harness AI meaningfully, it needed to be embedded into the fabric of the organisation."
That led DBS to build foundational capabilities across data, technology, processes, and talent, while developing ADA (Advancing DBS with AI), its enterprise data platform. The platform is designed to provide governed, discoverable, and high-quality data within a single environment and has since become a key component of the bank's AI strategy.
Singapore-headquartered DBS Group reported record first-quarter 2026 total income of SGD 5.95 billion ($4.6 billion) and net profit of SGD 2.93 billion ($2.27 billion). The lender had total assets of SGD 935.4 billion ($725 billion), customer loans of SGD 453.2 billion ($351 billion), and customer deposits of SGD 629.9 billion ($488 billion).
Mittal said AI gradually began influencing customer engagement, risk management, operational efficiency, and decision-making across the organisation, marking what he describes as an inflection point in DBS' AI journey.
"Our vision is to become an AI-enabled bank with a heart, blending the best of machine capabilities with human empathy, creativity, and judgement to deliver trusted banking solutions for our customers and employees," he affirms.
Enterprise-Scale AI
Mittal says one of the most significant developments in DBS' AI journey was the transition from isolated AI projects to repeatable deployments that could be scaled across business units.
He points to ADA, GenAI Flock, the bank's enterprise AI gateway, and its internal knowledge repository as important enablers of that shift.
"For us, transformation is reflected not in isolated use cases, but in the ability to deploy AI securely, responsibly, and consistently across customer journeys, risk management, operations, and employee productivity," Mittal notes.
The Numbers Story
DBS disclosed that the wider DBS Group has delivered 430 AI use cases across customer-facing businesses and support functions, and has built more than 2,000 models. These applications span retail banking, corporate banking, operations, and employee productivity functions.
ADA remains central to this effort. DBS describes it as an AI-ready enterprise data platform that supports the ingestion, orchestration, and deployment of analytics and AI workloads across hybrid multi-cloud environments. The bank notes that the platform also supports traditional AI, generative AI, and emerging agentic applications. However, while DBS provided figures on the number of use cases and models deployed, it did not disclose metrics related to productivity gains, cost savings, revenue impact, customer adoption, or return on investment.
Asked what distinguishes successful AI deployments, Mittal notes the strongest use cases tend to address clearly defined business problems, integrated into daily workflows and relying on strong data quality and governance.
"Long-term success also depends on whether a solution can be deployed responsibly, monitored effectively, and trusted by the people using it," he adds.
Responsible AI and the PURE Framework
DBS says responsible AI remains a central part of its deployment strategy through its proprietary PURE framework, which evaluates AI use cases on whether they are purposeful, unsurprising, respectful, and explainable.
The framework has led the bank to modify certain AI deployments before scaling them.
"In practice, there have been instances where we have refined use cases before scaling them, particularly in areas involving customer personalisation," he explains.
Beyond model performance, the framework also evaluates whether customer experiences align with expectations and whether AI-generated outcomes can be explained transparently.
Why DBS Built ADA and ALAN
DBS has invested in building several internal AI capabilities, including ADA and ALAN, its AI protocol, and knowledge repository.
According to Mittal, the bank views certain AI capabilities as strategic assets that require internal ownership.
"Building platforms such as ADA and ALAN, our AI protocol, and knowledge repository, in-house gives us greater control over governance, security, scalability, and integration within our operating environment," he shares.
The bank continues to work with external technology players where appropriate. However, DBS did not identify any technology vendors, hyperscalers, AI model providers, IT services firms or strategic AI partners involved in its AI initiatives.
The bank also declined to respond to questions on whether DBS works with global hyperscalers, Indian IT companies, foundation model providers, AI startups or specialised agentic AI firms, and how it decides between building capabilities internally, sourcing them from vendors or developing them jointly with partners.
Personalisation and Employee Productivity
DBS’ approach to hyper-personalisation is guided by customer context, timing and responsible data-use principles. "The distinction often comes down to context, timing, and customer expectations," Mittal says.
The bank uses reminders, insights, and proactive recommendations to improve customer experiences while maintaining trust.
Internally, DBS reported that AI adoption has expanded through tools such as DBS-GPT, its proprietary generative AI assistant that provides employees with role-based access to internal knowledge. The tool helps employees retrieve information more efficiently, support drafting tasks, and solve problems faster.
It also uses AI-powered assistants in customer service and CodeBuddy, a tool used by data professionals to support coding, exploratory analysis, and model development.
"Our experience shows that adoption is driven as much by people and culture as by the technology itself," he adds.
Lessons From Early AI Initiatives
Mittal recalls that some of the bank's earlier AI projects reinforced the importance of focusing on business outcomes rather than technology alone. "To deliver sustained impact, AI efforts must focus on clearly defined problems, supported by high-quality data, and be embedded seamlessly into workflows and decision-making processes," he affirms.
The bank has also invested in workforce readiness through its Triple E framework—Education, Exposure and Experience—which is designed to build AI literacy across the organisation. The programme aims to provide employees with foundational knowledge, practical exposure, and hands-on experience with AI tools.
Human Oversight Remains Critical
Despite the growing use of AI, DBS said certain decisions will continue to require human judgement, such as complex credit assessments, fraud, and risk investigations, and customer support situations where empathy and context is required.
"At DBS, we see AI as a tool to augment human judgement," Mittal remarks.
The bank's human-in-the-loop approach combines AI-driven analysis with human oversight, accountability, and ethical judgement. "Human oversight remains critical in ensuring decisions are fair, explainable and aligned with customer interests," he adds.
Looking ahead, DBS expects AI to become more deeply embedded across customer experiences, employee workflows, and decision-making processes. However, Mittal believes the technology alone would not define the bank's future.
"At the same time, the real differentiator will not be the technology itself, but how it is combined with human empathy, judgement, and trust," he said.
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