UOB achieved 99% cash availability at ATMs while reducing restocking trips by 30% – cutting operational costs and lowering the bank’s carbon footprint – while boosting engagement with omnichannel customers via AI for personalized banking experiences and real-time tailored insights.
Headquartered in Singapore, UOB is the third-largest bank in Southeast Asia in terms of total assets.
The bank provides services to personal, commercial and corporate customers, alongside asset management and insurance. UOB is focused on building the future of ASEAN through a global network of around 500 offices across 19 countries.
For nearly nine decades, UOB has adopted a customer-centric approach to create long-term value by staying relevant through its enterprising spirit and doing right by its customers.
Today, this takes the form of how the bank leverages data and insights to create personalized banking experiences and solutions catering to each customer’s unique needs and evolving preferences.
For that and more, UOB was awarded a winner of the Data Impact Award at Cloudera EVOLVE 2025.
Remus Lim, Senior Vice President, Asia Pacific & Japan, Cloudera, said: “UOB was recognized for its outstanding technical leadership in transforming its data infrastructure to power real-time, personalized banking experiences at scale.”
“The bank’s hybrid data architecture, self-service analytics capabilities, and end-to-end AI lifecycle management exemplify how technical innovation can be executed responsibly and at scale. This award reflects UOB’s commitment to leveraging trusted data and AI to deliver faster insights, richer customer experiences, and measurable business impact across the organization.”
Making service personal
For several years, UOB has been adopting artificial intelligence (AI) across many parts of the business to provide better experiences for their customers.
“By building a unified, enterprise-wide data foundation on Cloudera, the bank modernized its architecture to support advanced AI and ML across critical customer and operational touchpoints,” said Lim. “This included the deployment of scalable ML models that deliver hyper-personalized insights through the UOB TMRW app, optimize ATM operations, and enhance risk monitoring with greater precision and speed.”
The bank takes an omnichannel approach, using both physical and digital channels for all its banking needs, including apps, branches, and automated machines.
However, UOB faced several challenges in managing its data, such as data silos, inconsistent data quality and a lack of centralized governance. Over the last couple of years, UOB has consolidated more workloads to Cloudera on premises. This transition has enabled UOB to manage the full data lifecycle effectively.
Today, the bank relies on Cloudera’s data platform for collection and processing, including a built-in data science workbench for ML. Cloudera is the foundation upon which the company delivers a data-driven customer experience.
Personalization is one of UOB’s key pillars, focusing on communicating with customers in ways that work for them. AI helps the bank better understand and anticipate customer needs through several deployments, including:
- Managing ATMs effectively: AI optimizes UOB’s network of around 600 self-service machines across Singapore. Accounting for 20% of all customer transactions, the machines need to be always stocked.
- Creating personalized offers: Through the UOB TMRW app, offers are delivered in real-time and curated based on personal spending and saving habits.
- Predicting busy times at branches: AI helps customers plan the timing of their visits.
Benny Chan
Managing Director, Group Channels and Digitization, UOB, Cloudera.
According to Benny Chan, Managing Director, Group Channels and Digitization, UOB, Cloudera enables the bank to give its customers a truly personalized service.
Chan said: “Our all-in-one UOB TMRW app, retail branches and self-service machines are all essential touchpoints to provide a seamless and convenient banking experience for our customers. We also found that omnichannel customers are the most engaged with the bank, transacting up to 18 times more than traditional customers in 2023.”
Keeping ATMs ticking
UOB faced the challenge of optimizing its network of around 600 self-service machines, or ATMs, across Singapore. The bank wanted to reduce the operational cost and frequency of cash refilling trips while maintaining high service standards by ensuring cash availability and customer convenience.
To do this, the bank needed to understand the transaction patterns and seasonal trends of different ATMs, as well as the impact of various factors such as location, day of the week, time of day, and any special calendar events such as the Lunar New Year.
With Cloudera, UOB took a hybrid approach of using multiple forecasting models for each machine to predict the requirements, retraining multiple models as part of the process.
Cloudera has enabled UOB to ensure 99% cash availability and reduce 30% of trips to restock machines, saving operational costs and also supporting the bank’s sustainability goals by reducing its carbon footprint.
ML and AI drive personalized offers through the UOB TMRW app
By using Cloudera’s ML and AI capabilities, UOB has personalized its customer interactions and recommendations at scale through the UOB TMRW app, providing personalized insights and product offerings catered to every customer’s unique needs.
Alvin Eng, Head of Enterprise AI & Analytics Transformation, UOB, explained:
“For instance, we serve personalized insights cards in real-time to customers using data harnessed by AI, curated based on their personal spending and saving habits.
“This includes push notifications which proactively inform them when they are nearing the next interest rate tier, or reminders for credit card payments, to help customers better manage their finances.
For UOB, the service brings tangible benefits, such as sending more than 110 million personalized messages to over 2.5 million customers in 2022 alone. In 2023, UOB served close to 180 million personalized insights cards to more than 2.5 million unique customers.
“One key advantage of using AI is that now we can do things at scale,” said Eng. “For example, we can now reach out to millions of customers in highly personalized ways through UOB’s key touchpoints for customers. This has allowed us to maintain high levels of engagement with our customers and high customer satisfaction.”
Alvin Eng
Head of Enterprise AI & Analytics Transformation, UOB.
Looking ahead, UOB is adopting Generative AI to assist employees with tasks such as writing, research, and ideation. The bank is currently testing this technology using Cloudera Machine Learning (CML) in the public cloud to enhance its services and improve customer experiences.
Branch crowd prediction
UOB has also used an AI model to predict the crowd levels at the bank’s branches across Singapore. With this knowledge, customers can reduce their waiting times at branches by planning the timing of their visit to when the bank is less busy. This leads to happier customers and, ultimately, higher customer retention.
“UOB has demonstrated a strong ability to integrate AI deeply into business processes while upholding rigorous governance, security, and performance standards required in financ
