Karthik Shenoy outlines how the bank is using AI to augment advisers with ‘next best conversations’ while preserving trusted client relationships.
In private banking, trust and discretion have long been built face-to-face, over detailed conversations about markets, portfolios, and ambitions. At Bank of Singapore, technology is no longer just supporting that model—it is becoming a core driver of it.
Karthik Shenoy, managing director and head of the bank’s platforms and integration office, says artificial intelligence is now embedded in the bank’s strategy, not as a replacement for relationship managers, but as a means to make them more effective. “It’s not just about technology for technology’s sake—it’s about making banking better for everyone involved,” he says.
“Private banking has always been and continues to be a relationship-based business,” says Karthik Shenoy, managing director and head of the Platforms and Integration Office.
“Our strategy is to enable our advisers with the ‘next best actions’ and ‘next best conversations’ so they can focus on meaningful discussions about investment strategies, risk management, and opportunities,” he added.
AI co-pilots take centre stage
The bank’s digital transformation rests on what Shenoy calls an ecosystem of “co-pilots.” Product Copilot, Research Copilot and the Source of Wealth Assistant all help advisers navigate vast quantities of data in seconds. Meanwhile, HOLMES AI referred as the bank’s “CIO co-pilot” generates product content on the fly based on CIO research. The bank indexes more than 4,000 securities and 100,000 research highlights to generate curated product content for client conversations instantly.
“The idea is simple,” Shenoy explains. “These tools take care of the heavy lifting so our teams can spend their time with clients, not spreadsheets.”
Upcoming tools include a generative AI fund query system that will let advisers compare funds and prepare client pitches in minutes. A Day Board Propensity Model for equities already flags the most relevant trade ideas for clients, allowing quicker execution.
Efficiency beyond the client interface
While the front office benefits from sharper insights, Shenoy notes that the largest gains so far have come behind the scenes. AI-driven network detection techniques now identify links between new prospects and existing clients, incorporating proximity scores, adverse news and sentiment analysis.
On compliance, an Alert Prioritisation model uses machine learning to risk-rank alerts, ensuring higher-risk transactions are escalated promptly. “It’s about scale and precision—processing more data without diluting oversight,” he says. The approach has already earned external recognition, including a ‘Best Use of AI’ award.
Balancing in-house and external innovation
Shenoy stresses that success does not hinge on building every system internally. Bank of Singapore chooses to develop proprietary platforms when they can deliver a lasting competitive edge, while relying on partnerships to accelerate delivery elsewhere.
“We tend to build internally when we see a real opportunity to create a competitive advantage,” Shenoy says. “At the same time, partnerships with firms like Microsoft and SAS help us move faster without having to start from scratch.”
This hybrid approach, he adds, reflects the realities of AI adoption in banking: differentiation comes from design and integration, not just the underlying algorithms.
Data remains the constraint
Like its peers, the bank recognises that data quality is the limiting factor. “Garbage in, garbage out still applies,” Shenoy says. Clean, timely and complete data is essential for accurate AI-driven insights, yet ensuring this across the organisation involves cultural as well as technical change.
The bank is investing in a centralised data service ecosystem designed to give teams across functions access to consistent, reliable information. “It’s one of the biggest challenges—but also one of the biggest enablers,” Shenoy says.
A gradual revolution toward 2030
Shenoy envisions AI systems capable of mapping knowledge graphs from unstructured data, capturing every client interaction to anticipate outcomes with striking precision. “We’ll be able to predict outcomes—even the subtle ‘butterfly effects’ most people miss,” he says.
The transition will likely feel incremental, but its impact could be transformational. “When we look back in five years, the changes will feel revolutionary,” Shenoy predicts. For now, the bank is taking measured steps—piloting new tools, learning from practice, and scaling only when results prove reliable.
“AI isn’t just a passing trend—it’s a game-changer,” Shenoy concludes. “But its real value lies in augmentation. The human relationship will remain central to private banking.”
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