Unlocking the vision for AI in banking

ROUNDTABLE PANELLISTS:

• Praveen Raina, head of group operations and technology, OCBC
• Lawrence Goh, head of group technology and operations, UOB
• Evy Wee, regional head of wealth platform and solutions, DBS Bank
• Ashmita Acharya, head of international wealth and premier banking, HSBC Singapore

1. What do you see as the most significant artificial intelligence-powered innovations that will be pivotal in shaping or refining services for investors?

Praveen Raina: While the financial industry is still in the early stages of unlocking AI’s full potential, its ability to drive hyper-personalisation and predictive analytics is already reshaping how we engage with investors.

These capabilities allow us to shift from reactive servicing to proactive, insight-driven engagement, redefining how advice, portfolio construction and client interactions are delivered.

As we enter a new chapter with agentic AI, we are bringing greater intelligence to financial services and elevating the customer experience. A recent example is our source-of-wealth tool for private banking, which automates what used to be a manual and time-consuming process, significantly reducing the time taken for relationship managers to draft reports from 10 days to an hour.

Relationship managers now simply review and refine the AI-generated report, freeing up valuable time to focus on deeper client conversations and sharper risk assessments. The result is stronger relationships and greater value delivered to both the customer and the business.

Banking is, at its core, a relationship business. For investors, this means expecting experiences that are exceptional and tailored to their unique financial goals. As AI continues to evolve, the ability to dynamically tailor portfolios and communication to each individual will become the norm, where service is deeply personalised.

Lawrence Goh: Traditional investment management methods often rely on historical data and static models. With the advancement of AI, investment managers are now empowered to synthesise vast, real-time datasets and anticipate market movements, enabling a shift from reactive to proactive portfolio management.

Investment managers can leverage AI to analyse large and complex datasets to uncover patterns and generate actionable insights, helping to identify promising investment opportunities.

These AI-generated suggestions can then be curated by human experts, who apply their market experience and judgment to validate and refine investment selections for customers.

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For instance, UOB Private Bank harnesses successfully validated AI models to enhance the productivity of the portfolio managers and improve the quality and consistency of decision-making. Such technologies have greatly aided our portfolio managers in collating and organising the necessary information and data across multiple sources, enabling more proactive risk management, supporting our engagement and advisory to clients.

AI can also be used to assess customers’ profiles, including risk tolerance, investment horizon, current investment holdings, financial goals and personal preferences to build a comprehensive understanding of their investment needs.

This facilitates the delivery of tailored investment recommendations that align with customers’ needs and risk profiles while factoring in market trends and best-in-class portfolio strategies.

UOB TMRW, our all-in-one banking app, is powered by AI and machine learning models to deliver personalised insights with curated financial content, expert analysis and relevant investment advice catered to customers’ banking behaviour and preferences.

While AI can enhance efficiency and personalisation at scale, it has its limitations. It cannot replace the human touch that is vital to building lasting client relationships – especially in banking, where trust is paramount.

For instance, a customer may not have accurately reflected their risk profile, or their profile may evolve over time. A relationship manager who understands the customer’s unique circumstances and preferences is best placed to provide tailored investment advice, with AI serving as a support tool to complement and strengthen that advisory process.

Moreover, AI systems are trained on historical data and past trends. In the face of black-swan events that cause sudden market disruptions, they may fall short in providing timely and relevant recommendations. This is where human experience becomes indispensable, juxtaposing market nuance with AI insight to deliver more contextual and suitable options for the customer.

Companies also need to be mindful that the adoption of these technologies comes with potential risks – including model hallucinations, data privacy risks and output biases – which must be actively managed.

Thus, we take a prudent and responsible approach to AI adoption. We have established strong governance frameworks to ensure our models align with the Monetary Authority of Singapore’s Fairness, Ethics, Accountability and Transparency principles.

Evy Wee: Over the years, the development of financial technology, digitalisation and data capabilities have enabled financial institutions (FIs) to scale the adoption of financial services to greater numbers of customers worldwide. Frictionless payments, democratisation of wealth management and fraud protection are some of the key examples of the new services that have emerged, benefiting consumers at large.

As the capabilities of data evolve through better and more intelligent models, AI has also been a key enabler to deliver personalised financial advice to consumers, allowing them to take concrete actions to improve their financial health.

At DBS, we have observed that customers who are highly engaged with our digiWealth platform saved more, have sufficient protection in emergencies and they are also staying invested, even amid the recent market volatility.

Our ability to harness data sharing by customers through Singapore’s open banking infrastructure (SGFinDex) have given us better understanding of customers’ asset and liabilities trends through their life stages.

Gaining such insights allowed us to uncover the gaps in both accumulation and the decumulation phases of retirement in Singapore. This led DBS to launch very first retirement glidepath portfolio in Asia, with the aim of helping people of all ages get started with their retirement planning, partnering JP Morgan Asset Management.

Looking ahead, as AI continues to evolve rapidly, generative AI (GenAI) and Agentic AI present opportunities for organisations to rethink and transform existing operating models, such as developing co-pilots or agents for various roles within the end-to-end process of product curation, manufacturing, execution and safekeeping of financial assets.

We are also observing how our private bankers and investment counsellors have been able to more efficiently and effectively generate personalised investment recommendations, wealth planning and market insights to better engage clients.

Through combining the foundational technology we have developed such as digitalisation, automation of workflows together with AI/machine learning (ML) and large language models (LLMs), advancements in AI will enable FIs to reduce toil for their employees, enhance productivity and allow more time for higher value work such as advisory and deeper client engagement.

More importantly, with AI, FIs can also deliver hyper-personalised insights to empower customers to make better financial decisions for themselves.

Ashmita Acharya: AI is transforming how clients engage with wealth advisory by making wealth conversations more personalised, responsive and insight driven.

That is why we are investing not only in the technology, but also in upskilling our people so they can harness new tools to deepen the client-advisor relationship.

Wealth management is built on trust, and the role of the adviser has never been more important. The most exciting innovation for us is AI’s ability to help our advisers anticipate needs, deliver sharper insight and help clients make timely, forward-looking decisions.

One key area driving this shift is predictive analytics, which includes real-time risk and opportunity sensing. With GenAI and natural language processing (NLP) capabilities, we can now extract insight from sources that were previously too vast – such as earnings calls, analyst reports, even satellite data.

Such deep insight is helping advisers identify risks and opportunities much quicker and equipping them to guide investors towards more proactive and evidence-based choices.

For example, within our global private banking business, our proprietary Wealth Intelligence platform uses GenAI to synthesise insight from more than 10,000 data sources, enabling advisers to deliver more proactive, informed and strategic portfolio recommendations.

AI today can analyse far more than just clients’ risk appetite. We now have the ability to capture a client’s broader financial life – from spending and saving patterns to liquidity needs and environmental, social and governance (ESG) preferences – giving us a comprehensive, holistic view.

This means that advice can be hyper-personalised, a significant shift from reactive advice (“what would you like to invest in?”) to proactive guidance (“here’s what you may want to consider”).

Within our wealth platforms, we are leveraging these insights to deliver dynamic portfolio insight, tailored nudges and curated educational content to help our clients make more informed and confident financial decisions.

From a more operational lens, AI is also empowering advisers by reducing administrative effort so they can focus on what matters most – the client. Our advisers are now using AI tools to summarise client conversations and prepare concise pre-meeting insight through tools that distil client portfolios, sentiment and market developments into actionable summaries, leading to more strategic and meaningful wealth conversations.

While AI has vast potential to enhance how we work and serve our customers, the combination of technology and people will remain key to how we use AI.

We are also highly aware of AI’s potential risks, and are working with regulators and partners to help develop a robust regime that supports ethical innovation. This allows us to leverage the complementary strengths of AI’s processing power alongside expert human judgement in line with our core principles of responsibility and trust.

2. How can AI help to enhance customer loyalty?

Praveen Raina: Trust is the foundation of customer loyalty – trust in the bank’s stability, systems and service. While products matter, the differentiator lies in the consistency of our delivery and service.

AI is an enabler that allows us to scale while maintaining this trust. For instance, our AI-enabled surveillance systems strengthen fraud detection and anti-scam efforts, while our “defensive AI” capabilities proactively detect and respond to cyberthreats and potential vulnerabilities. These reinforce system reliability and data security, which are critical pillars to retain customer confidence.

Beyond protection, AI enhances the everyday experience. Our AI systems make around six million decisions daily, helping deliver more personalised and relevant interactions.

For example, we use AI to analyse more than 10,000 pieces of customer feedback each month to identify opportunities to improve our products and services.

Ultimately, customer loyalty is earned – more than just through what we offer, but how reliably and securely we deliver it.

Lawrence Goh: AI enables organisations to create deeper, more personalised engagements and relationships with customers by anticipating what matters most to each individual. Organisations can tap into AI to analyse behavioural patterns, preferences and life stages to proactively offer relevant products, services or experiences pre-emptively.

As the bank with the most extensive network in Asean, UOB recognises the need to continuously learn about the diverse needs and preferences of our customers. AI has enabled us to scale hyper-personalised interactions across our entire customer base of more than 8.4 million, through our UOB TMRW app.

Our app, initially launched for a younger demographic, has since evolved into the bank’s key digital platform to acquire, serve and engage customers at scale across Asean.

With AI, we are able to tailor products and offerings to each individual’s needs and preferences, building trust and loyalty by demonstrating that we truly understand our customers and their financial aspirations.

Customers want to feel genuinely understood. Intelligent personalisation through AI fosters a relationship where customers feel “you know me, and you always suggest what is best for me”. This is the foundation of lasting customer loyalty, and the fundamental principle guiding the design and development of our app.

By leveraging AI to understand customers’ behaviours, we are able to enhance our value proposition to help customers save more, spend smarter and invest online.

Evy Wee: Personalised engagements and fit-for-purpose advice help foster trust and relevance, both of which are essential to long-term loyalty.

At DBS, we developed AI-powered hyper-personalised insights and advisory that enabled customers to make better informed financial decisions. In Singapore alone, more than 3.5 million retail and wealth customers interact with 30 million such insights each month across DBS digibank, PayLah!, and e-mail.

This personalised engagement approach is paying off. We observed that our wealth clients are diversifying their investments across geographies, industries as well as asset classes – the number of clients who hold two or more investment and insurance products has grown by 30 per cent year on year.

Among ultra-high-net-worth clients, net cash deployed to investments doubled last year and continues to be resilient in the first half of the year. This uplift has been driven by our prescient Chief Investment Office (CIO) insights, delivered to clients through AI-powered nudges.

As AI capabilities continue to develop, FIs need to ensure data is used in a responsible way that improves the business as well as safeguards our customers. In DBS, we have embraced a robust framework that guides ethical AI development which we call PURE – in which we ensure that data must be purposeful, unsurprising, respectful and explainable for all our customers.

We were recently named World’s Best AI Bank by Global Finance magazine, which underscores the strides we have made in harnessing responsible AI and delivering customer-centric solutions through strategic integration of AI across the bank.

As DBS advances on our journey to be an AI-enabled bank with a heart, we remain committed to leveraging AI responsibly, blending machine intelligence with human empathy to reinforce the trust our customers place in us.

Ashmita Acharya: In wealth management, loyalty is built on how relevant our advice is to our clients, how positive their experiences are, and how meaningfully we stay connected over time.

At HSBC, we are integrating AI across our advisory ecosystem to offer a more anticipatory, efficient and personalised customer experience – one where technology enhances responsiveness and enables a deeper understanding of what our clients need.

For example, by drawing insight from how clients manage their portfolios and interact with us, AI helps us anticipate important life moments, identify emerging needs and reach out with guidance that feels more intuitive. This shifts the experience from being reactive and transactional to proactive and personalised, helping clients feel understood, supported and valued.

But it is not just the big moments – we are also leveraging AI to elevate the everyday client experience, from customer service to engagement and outreach.

By analysing preferences and interaction patterns, for example, we can personalise our communications to ensure that the insight, market updates and educational materials we share are relevant and valuable, deepening engagement and trust. The outcome has been faster response times and higher satisfaction scores, with smoother and more consistent experiences delivered across touchpoints.

Finally, we are also leveraging AI to listen better. By analysing sentiment and feedback across channels, we can identify shifts in confidence or satisfaction early and take action before issues arise.

3. The ability to give bespoke advice efficiently is something of a holy grail for financial services. Where are you at the moment in this effort, and how can you deliver tailored advice even for the mass customer base?

Praveen Raina: We have already introduced several AI-powered tools such as Holmes AI, AI Oscar and Markets Watch, all of which deliver personalised insight across customer segments – from curated investment insight for private banking clients, to tailored stock ideas for trading customers and timely market updates for businesses.

Bespoke advice is rapidly becoming an expectation. To scale this, AI is being embedded deeper into our software engineering and quality assurance workflows. For instance, our engineers now leverage AI-driven development tools to rapidly prototype and iterate personalised advisory engines, to generate dynamic recommendations based on individual goals, behaviours and life stages.

On the quality assurance front, AI simulates thousands of customer scenarios to validate that advice remains accurate, relevant and compliant. This ensures our solutions are robust enough to serve millions without compromising on the personal touch.

By integrating AI into software engineering, we can scale bespoke advisory at speed and deliver it across all customer segments, transforming what was once a premium offering into a universal standard.

Looking ahead, our goal is for 75 per cent of all customer service requests to be AI-assisted by 2027. This will reduce response times and enable faster, more personalised products and services across the board.

Lawrence Goh: Once exclusive to high-net-worth (HNW) individuals, personalised engagements and bespoke investment strategies are now accessible to the mass market, thanks to AI.

UOB harnesses AI to scale intelligent personalisation across financial advisory:

• Tailored investment content and advisory: Powered by AI and machine learning models, UOB TMRW app delivers investment content and advisory tailored to each customer based on their demographics, banking behaviours and preferences, ranging from beginner-friendly investment guides for younger customers to expert macroeconomic insights and analysis for seasoned investors.
• Dynamic portfolio advisory: AI continuously learns and analyses customers’ banking behaviours and portfolios changes, enabling our bankers to offer proactive and tailored advice that align with evolving financial goals and market conditions.
• Digital-first, human-backed: UOB TMRW is our key growth engine. We harness AI to understand customer preferences through their interactions with the app, anticipate their needs and deliver personalised recommendations that empower confident financial decisions. This is complemented by our comprehensive physical network, creating a wealth continuum that delivers personalised experience throughout each customer’s financial journey.

Evy Wee: In Singapore, where everyone has access to basic banking services, the underserved usually refer to those who do not have access to quality advice, which is much needed to address their financial gaps and ensure retirement adequacy as our society rapidly ages.

Our research shows that our Gen Z and millennial retail customers are falling behind their older counterparts in building their retirement nest eggs. This younger group of customers allocates only 15 to 17 per cent of their income to investments, which is insufficient for long-term financial security, especially when compared to pre-retirement age groups (45 to 64) who allocate 30 to 49 per cent.

People need to understand that saving alone will not be sufficient. Investing, especially with the effects of compounding, would help to fuel wealth growth. Yet, some people are still sitting out of the financial markets because of the misconception that they need to depend on someone else to invest or that they need a ton of money to do so.

Over the years, banks and fintechs have changed all that. At DBS, we’ve been able to tap data and AI to implement intuitive and seamless ways for our customers to get access to tailored advice and solutions that enable them to participate in the markets, whether with their first S$100 or S$1,000.

For financial advice to be effective, it needs to be personalised and more importantly, fit for purpose. We’ve built over 100 AI/ML models that analyse data from more than 15,000 customer attributes, from transaction patterns and lifestyle choices to risk appetites and key milestones to generate these insights.

Those in Singapore who engaged with these nudges in 2024 saved twice as much, invested five times more, and were nearly three times more adequately insured than non-users.

We are looking to replicate our successful model in Singapore across Asia, which includes Taiwan, Thailand, Hong Kong, India and Indonesia. By 2027, we hope to quadruple the number of customers who can access our financial advice and solutions in these markets.

Ashmita Acharya: Bespoke advice has always been central to wealth management, particularly in the HNW space. But what is changing today is how technology is helping us deliver high-quality, personalised advice at scale.

For our affluent and emerging-affluent clients, we have made significant investments into AI-powered wealth platforms that make personalised guidance more accessible and actionable. These platforms combine data-driven insight with predictive analytics and goal-based planning tools to help clients make more informed decisions about their wealth.

With the support of curated educational content, bite-sized investment insight and timely nudges, these platforms have helped us deliver relevant, needs-based advice at scale that supports clients as they progress along their wealth journey.

Across segments, including for our HNW clients, our relationship managers are being empowered with deeper and more timely insight. AI-driven dashboards such as our Virtual CIO tool provide real-time market intelligence, portfolio risk indicators and scenario analysis – helping advisers identify new opportunities, anticipate market shifts, and further tailor strategies to each client’s goals.

These advancements play directly to HSBC’s strengths. As a universal bank, we have the ability to integrate capabilities across wealth management, asset management and insurance, supported by a global network and deep regional insight. As clients’ financial lives become more complex and interconnected, this integrated model allows us to deliver precise, integrated advice across wealth, health, investment and protection.

With AI, we are taking these strengths to the next level – using data and technology to deepen personalisation, sharpen insight and deliver truly holistic advice that is as connected and dynamic as our clients’ needs.

4. More Asian investors want to invest sustainably and/or achieve impact. How can AI help?

Praveen Raina: Sustainable investing is a fast-growing priority, especially among affluent and impact-conscious investors in Asia. As expectations rise for investments to deliver both financial returns and positive change, AI is emerging as a game changer, enabling investors to make better informed, values-aligned decisions.

Its strength lies in processing vast volumes of data and applying predictive analytics to uncover deeper ESG insight. AI can build a real-time, comprehensive view of a company’s sustainability performance by analysing data across multiple sources.

It can also forecast future ESG performance, detect greenwashing by cross-referencing claims with actual data, and identify emerging sustainability trends. These capabilities empower investors to make better decisions with confidence.

As demand grows, AI will play a central role in enabling smarter, more transparent and impact-driven investment decisions.

Lawrence Goh: For investors seeking to align their portfolios with sustainable values, AI offers a powerful edge through deeper and more dynamic ESG analysis.

AI enables real-time tracking of ESG metrics from a wide range of sources – including including news reports, corporate disclosures, ratings and alternative data such as social media – enabling investors to make more timely and informed investment decisions. This is particularly relevant for Asian investors, who are increasingly prioritising sustainability.

According to UOB’s Asean Consumer Sentiment Study 2025, close to 40 per cent of consumers have invested in sustainable investments such as green bonds, with more than one in 10 increasing their holdings. AI also allows both investors and organisations to tailor portfolios that reflect not only financial objectives but also personal sustainability preferences.

Despite growing interest in sustainable investing, navigating the region’s diverse regulatory landscapes and varied market maturity levels can be a challenge.

As ESG disclosure requirements tighten globally, including in key Asian markets, AI helps bridge these gaps by offering consistent, data-driven insights aligned with both regional and global ESG standards. It also enhances due diligence, ensuring investments meet both regulatory standards and internal sustainability benchmarks.

AI can also help address growing concerns around greenwashing by detecting inconsistencies or misleading claims in ESG disclosures through tools such as NLP and sentiment analysis. This safeguards against misrepresented products, which is increasingly crucial in Asia’s fast-evolving ESG ecosystem, where disclosure practices may vary widely.

Evy Wee: AI provides the precision and scale required to confidently execute the companies’ dual mandate of profit and positive social and environmental return.

At DBS, we integrate GenAI tools into the ESG risk questionnaire for client due diligence. This “fact-checker” screens for red flags and streamlines compliance, reducing staff workload.

AI also helps us measure the actual positive outcomes of our financing. AI models help curate high-impact solutions, such as ESG funds, by aligning capital with crucial Asian themes, including renewable energy.

Ashmita Acharya: We are indeed seeing this shift across Asia. Many next-generation investors want portfolios that align financial performance with purpose, and we have seen strong client interest in sustainability themes such as energy transition, resource efficiency and social inclusion.

We are also helping families explore philanthropic and impact-driven strategies that contribute to long-term causes such as climate resilience and education.

Increasingly, clients want to understand the real impact their portfolios can create, and how to balance performance with purpose. Harnessing AI makes this possible by enabling greater transparency, measurability and personalisation.

For example, a long-standing challenge has been the quality and consistency of ESG data. AI is transforming this with the ability to process information from thousands of sources – from company disclosures to satellite imagery – to validate environmental and social claims, and identify greenwashing risks. This gives investors a clearer, more objective view of what is really happening on the ground.

Similarly, AI is also enabling impact measurement at scale. NLP tools can extract and quantify indicators such as carbon emissions avoided or social outcomes achieved, helping investors track progress against the causes they care about.

As advisers, our role is to harness the power of AI to help our clients invest for impact with both clarity and conviction, using a data-driven approach that turns value-based aspirations into meaningful investment strategies that also meet long-term financial goals.