Blueprint for Banking in the AI-era: Nimish (DBS)
Chief Data & Transformation Officer @ DBS Bank
Nimish Panchmatia is the Chief Data & Transformation Officer of DBS - the largest bank in Southeast Asia.
He joined DBS in 2010 and has been at the center of one of banking’s most ambitious AI transformations. Today, DBS runs over 2,000 AI models across 430 use cases - delivering about SGD $1 billion in economic value in 2025. In this post, Nimish shares how DBS built that foundation, how agentic AI is reshaping the way organizations work, and what it means to be an AI-enabled bank with a heart.
Q: DBS has been consistently recognized as a front‑runner in AI adoption among global banks. At a strategic level, what are the principles and frameworks that have guided your AI journey - from experimentation to enterprise‑wide deployment?
Nimish: We began experimenting with AI as early as 2014, and since then we have done the heavy lifting to clean our data, place it in data lakes, and build the right architecture for us to unlock the power of data.
First, we re‑architected our technology stack to be scalable, robust, and automation‑ready. This meant moving away from legacy systems to open‑source, and investing in a hybrid, multi‑cloud infrastructure to unlock compute power and flexibility. At the same time, we built in‑house capabilities like:
ADA – our self‑service data platform and single source of truth, ensuring governance, discoverability, quality, and security.
ALAN – our AI protocol and knowledge repository, creating a standardized, repeatable approach to deploying models across use cases.
We also centralized all 700 Data Professionals under a Data Chapter, to scale up value from Data Analytics and AI/ML by developing a pool of deep experts to deliver business outcomes at scale and at speed.
These efforts enabled us to move from experimentation to enterprise scale. Today, DBS runs over 2,000 models across 430 use cases, touching every part of the bank – from customer engagement and risk management to operations and employee enablement. This foundation also positions us well to rapidly scale Gen AI and Agentic AI use cases.
While technology and process were critical, the people element was equally important. We were, and continue to be committed to bringing employees along on this transformation – through upskilling, AI literacy programmes, and embedding data specialists into cross‑functional squads aligned to customer journeys.
Finally, trust underpins everything. Our Responsible Data Use (RDU) framework and PURE principles (Purposeful, Unsurprising, Respectful, Explainable) guide every initiative, supported by strong governance and human‑in‑the‑loop controls. We also work closely with regulators and industry bodies – leading MAS’ MindForge Data & AI workgroup, contributing to the AI Risk Management Executive Handbook, and shaping Generative AI guardrails for banking.
Our efforts have allowed us to scale AI responsibly and earn global recognition, including winning Global Finance’s “World’s Best AI Bank” in 2025.
Q: Could you share some of your AI use cases and their impact to the bank and its customers?
Nimish: Our approach to AI is centered on delivering measurable business value at scale, while ensuring strong governance and trust. Fundamentally, we organize our AI deployments across horizontal use cases, which provide enterprise‑wide capabilities for all employees, and vertical use cases, which are deeply embedded within specific businesses, customer segments and job roles. This allows us to industrialize common capabilities across the bank, while applying AI in targeted ways that enhance customer experience, reduce employee toil and augment human judgment.
At the horizontal level, DBS‑GPT, a secure internal assistant, helps employees accelerate the search, synthesis and application of bank policies, institutional knowledge and internal content.
We have also deployed a range of vertical, role‑specific AI solutions:
DBS Joy, our Gen AI‑enabled banking assistant, was introduced in late 2025 to provide 24/7 support to corporate customers. Joy delivers dynamic, contextual responses through natural conversations, remembers past interactions and seamlessly escalates complex issues to live agents, contributing to higher customer satisfaction.
Wealth Co‑Pilot consolidates insights from multiple sources to help relationship managers deliver more tailored, relevant recommendations, augmenting human advice and improving the quality of client conversations without replacing human judgment.
In 2021, we were one of the first banks to quantify the economic value of AI. Last year, our data analytics and AI/machine learning initiatives delivered approximately SGD 1 billion in economic value in 2025.
Q: With the rise of agentic workflows and AI copilots, how do you see the shape of organizations evolving? Will this lead to leaner teams, new roles, or entirely new ways of working?
Nimish: At DBS, we expect leaner toil, not leaner teams. The fundamental shift is not about reducing headcount, but about redesigning roles and adopting an AI‑augmented way of working. As AI agents become more capable, they will increasingly complement our human workforce by taking on time‑consuming and repetitive tasks. This allows employees to focus on higher‑value work where human qualities such as judgment, creativity, empathy and complex problem‑solving remain essential and irreplaceable.
This transition is already reshaping how work is organized across the bank. We are redesigning job roles and redeploying employees into new career pathways as AI changes the nature of frontline and specialist work. At the same time, AI adoption has created entirely new areas of work, including AI governance, risk management, agent oversight, and agentic AI development and engineering.
Upskilling is a critical enabler of this shift. At a horizontal level, all employees are equipped with a baseline understanding of AI and practical exposure to tools that reduce manual toil and enhance productivity. In parallel, we have identified more than 11,000 employees in AI‑impacted roles for deeper, role‑specific capability building through vertical use cases, ensuring they can work confidently and effectively alongside AI in their respective domains.
Q: Your CEO Su Shan has spoken of her vision of DBS as an AI‑enabled bank with a heart. What does that mean to you?
Nimish: As we continue to scale the use of AI across the bank to deliver a seamless One Bank proposition to our customers and supercharge our employees, we will do so while retaining the human elements of empathy, judgment, creativity and cultural nuance. In doing so, we want to elevate AI from just artificial intelligence to authentic intelligence, combining the best of machine and human qualities to deliver trusted banking solutions, while upskilling and reskilling our people to be future‑ready.