“This use case demonstrates how AI complements human work by serving as a strategic advisor to empower bankers to make informed decisions that align with business goals, thereby providing incremental, measurable value in a complex, financial environment,” says Richard Lowe, UOB’s chief data officer.
Following its domestic rollout, UOB is now deploying the solution across its regional markets, starting with Malaysia.
Focusing on ‘purposeful transformation’
For UOB, AI represents a strategic pillar anchored in what Lowe calls “purposeful transformation”. In a highly regulated industry where trust is paramount, innovation must be paired with rigorous governance.
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“We are not adopting AI for its novelty, but leveraging it to create meaningful impact for our customers, colleagues and the broader financial ecosystem,” says Lowe. “That means moving fast, but with discipline, transparency and a deep commitment to responsible innovation.”
All AI initiatives undergo ethical reviews and bias testing. Data is anonymised and used only with customer consent. The bank collaborates closely with regulators and ensures human oversight in critical areas, including fraud investigation and lending.
AI across operations and customer service
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UOB’s AI is used across multiple domains, including optimising cash replenishment at ATMs across Singapore. Powered by its data platform, AI-driven analytics forecast cash demand by location and time, enabling smarter replenishment schedules and ensuring ATMs are stocked when needed.
The results have been significant. After implementing this solution, the bank has seen up to a 33% reduction in replenishment trips, cutting unnecessary cash movements while ensuring ATMs remain adequately stocked.
On the customer-facing side, AI enables more personalised services and quicker support. Behind the scenes, it streamlines processes like document handling and compliance checks through automation, allowing employees to focus on higher-value tasks.
In risk management, AI supports early detection of potential risks across various compliance and regulatory areas, including anti-money laundering (AML) processes. “It plays a key role in helping us detect suspicious customer transactions and improve the efficiency of our monitoring systems,” says Lowe.
He adds that AML “is an area that requires continuous effort and collaboration, both within the industry and with regulators, as the financial crime landscape is constantly evolving and criminals get more sophisticated. We must stay ahead by continually refining our models, sharing insights and adapting quickly.”
Internal adoption metrics suggest AI is becoming embedded in the bank’s operations. Data platform requests from UOB employees reached nearly 6.5 million in the first nine months of 2025, up from about five million in the same period a year ago. “This growth validates the increasing relevance of data insights,” Lowe notes.
A hybrid approach
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UOB takes a hybrid approach to developing its AI capabilities, combining in-house talent development with strategic partnerships.
In October 2024, the bank launched the AI and Data Analytics Centre of Excellence (AIDA CoE) with the Infocomm Media Development Authority and the National University of Singapore, targeting 100 university graduates over three years. Internally, the bank has also rolled out training programmes on prompt writing and data visualisation, enabling employees to use generative AI tools like Microsoft Copilot effectively.
“Investing in internal expertise is essential for long-term success, as it allows us to embed AI deeply into our business, tailor solutions to our unique needs and foster a culture of data-driven decision-making across the bank,” says Lowe.
At the same time, working with technology providers gives the bank access to cutting-edge tools and proven methodologies, especially in areas where speed and scale are critical. UOB, for instance, uses Cloudera’s data and AI analytics platform to manage data seamlessly across on-premises and cloud environments. “With the right data infrastructure in place, we have successfully scaled several AI initiatives, [such as those focused on portfolio optimisation and customer engagement,] from proof-of-concept to full production over the years,” says Lowe, adding that they are now delivering real-time insights and measurable outcomes.
Agentic AI is the future
Asked about the future trajectory of AI in banking, Lowe points to agentic AI as a technology that could redefine banking services.
“By moving beyond task-based automation to autonomous decision-making, it introduces a new paradigm where intelligent agents can set goals, adapt to changing contexts and act proactively on behalf of users,” he adds.
The technology could enable banking services that anticipate customer needs, optimise financial outcomes and resolve issues before they surface. Agentic AI could support both customers and employees by offering personalised advice, automating complex workflows and enhancing operational resilience.
But these capabilities come with new governance challenges. “As these capabilities evolve, we must also adapt our approach to governance and trust accordingly,” says Lowe. “Transparency, accountability and ethical alignment will be critical as agentic AI becomes more deeply embedded in financial services.”
More broadly, Lowe sees AI reshaping banking across multiple dimensions, from internal operations to customer engagement and risk management. The industry is shifting from using AI for incremental operational improvements to reimagining processes entirely.
“At UOB, we have seen the potential that AI can bring and are now adopting this technology with a strategic lens,” he says. “We will continue to invest in our data infrastructure, talent pipelines, and governance frameworks that are needed to harness AI responsibly and sustainably.”
The 5% corporate banking revenue uplift underscores that disciplined, purposeful AI deployment can deliver measurable value while maintaining the trust that underpins the financial system
