​​​​​​​In a market where client demand is evolving faster than many product shelves can keep pace, the role of the asset manager is no longer confined to manufacturing funds and waiting for distribution. Today, it requires a constant reading of where flows are moving, what gaps exist in advisory portfolios, and how emerging tools such as artificial intelligence can be harnessed not merely as a marketing proposition but as a genuine input into investment decision-making.

At the Malaysia Wealth Management Forum 2026, hosted by Hubbis in Kuala Lumpur, an investment advisory panel chaired by Alex Ng, Managing Director and Head of Intermediary, Asia Client Group at Janus Henderson Investors, examined how firms are constructing portfolios, sourcing products, and scaling advice across client segments. Edwin Leong, Head of Product Innovation and Research at RHB Asset Management, offered a product originator’s perspective on where client flows are heading, why Malaysia’s fixed income landscape presents both opportunity and constraint, and how his firm is beginning to integrate AI into asset allocation in a way that removes emotional bias from the investment process.

Key Takeaways


Income-oriented strategies are dominating flows, with particularly strong demand for products that use call option premium strategies to deliver income in a more structured and repeatable manner.
Equity exposure is returning to favour, with clients allocating to technology, gold equity, and broad Asia ex-Japan strategies as confidence in listed markets recovers.
Malaysia’s fixed income market remains structurally local, dominated by institutional and GLC capital, though retail and bank distribution channels are increasingly open to differentiated strategies that can deliver above-market returns.
Currency hedging costs remain a binding constraint on the viability of offshore fixed income strategies for Malaysian investors, requiring returns to clear a meaningful hurdle before they make commercial sense.
AI is moving from experimentation to operational deployment, with RHB Asset Management launching a strategy that uses an AI overlay to determine monthly asset allocation, removing emotional decision-making from the process.

 

Following the Flows

When Ng asked how asset managers are developing new product ideas and filling gaps in wealth portfolios, Leong offered a concise snapshot of where client capital is moving in 2026.

“Flows for this year is we are seeing a lot of flows into income-oriented products, especially those strategies with call option premiums, so that income is listed in a way,” Leong said. The demand for structured income, where the yield is generated through options-based strategies rather than relying solely on traditional coupon or dividend income, reflects a broader shift in client expectations. Investors want predictability and transparency in how their income is produced, and strategies that can demonstrate a repeatable mechanism are attracting disproportionate interest.

Beyond income, Leong noted a parallel trend: the return of flows into products with full equity exposure. “We are also seeing flows coming back into products with full exposure into equity, be it your tech, be it your gold equity, or be it plain vanilla Asia ex-Japan equity,” he said.

The two trends are not contradictory. A participant observed that income-driven strategies inevitably involve trade-offs, particularly around giving up market beta at a time when indices are approaching new highs. Clients are increasingly aware of the opportunity cost embedded in pure income positioning and are seeking a balance between stable cash generation and participation in equity upside.

The Fixed Income Constraint

Ng steered the conversation towards fixed income, noting that high-quality onshore strategies tend to dominate the Malaysian market, while offshore fixed income products struggle to deliver attractive returns once currency hedging and fees are accounted for. He asked Leong directly whether the local fixed income market would always be limited to lower-rated, higher-yielding strategies, or whether clients could access safer options that are not purely domestically focused.

Leong confirmed that Malaysia’s fixed income landscape is heavily skewed towards local strategies. “For the fixed income space in Malaysia, yes, it’s predominantly dominated by the local funds, local strategies, where it is invested locally,” he said. The structural reason, he explained, is the weight of institutional and government-linked capital in the market. “There are a lot of corporate, institutional GLCs that are invested in those strategies or those funds,” he noted.

However, Leong drew a distinction between the institutional market and the retail and bank distribution channels, where appetite for differentiated fixed income is growing. “For people like ourselves, the IFAs, I think these channels are more open to choices or different selection of fixed income strategies,” he said.

The critical constraint remains the hedging cost. Any offshore fixed income strategy marketed to Malaysian investors must clear the hurdle of currency hedging and associated fees before it can claim to offer genuine value over local alternatives. “The returns have to make sense at the end of the day to the local investors,” Leong said. “The net returns,still has to make sense and deliver something that is above the local fixed income market.”

The implication for product designers is clear. Offshore fixed income strategies that work in US dollar terms may not survive the translation into ringgit-denominated returns. Unless the yield premium is sufficiently wide to absorb hedging costs and still offer a meaningful pickup, the product will struggle to gain traction with Malaysian investors.

AI as an Allocation Tool

Leong’s most forward-looking contribution concerned RHB Asset Management’s adoption of artificial intelligence as a tool for asset allocation. While the firm remains rooted in traditional, fundamental stock-picking, it has embarked on a new initiative over the past year that uses AI in a more operational capacity.

“We actually embarked on something to do with AI, launchinga strategy that is invested in non-traditional asset class, and we use an AI overlay to determine the asset allocation for us,” Leong said.

The mechanics are straightforward. On a monthly basis, the AI model generates allocation recommendations across asset classes, which can then be executed byour portfolio managers. The key value proposition, Leong argued, is the removal of emotional bias from the allocation decision. “There is this AI overlay where we remove all the emotional aspect of it by investing into that particular asset classes,” he said.

The approach sits at the pragmatic end of the AI spectrum. Rather than attempting to replace the entire investment process with machine learning, RHB has ringfenced a specific function, tactical asset allocation, and applied an AI model to that task alone. The fundamental research capability remains human-led, and the AI overlay operates as a complement rather than a substitute.

For the Malaysian market, where many asset managers remain in the early stages of AI adoption, RHB’s approach offers a useful case study. A targeted application, focused on a single decision point where emotional bias is a known risk, can generate measurable benefits without requiring a wholesale transformation of the firm’s investment philosophy.

Bridging Manufacturing and Distribution

Leong’s contributions across the panel highlighted the evolving relationship between asset managers and their distribution partners. In a market where actively managed funds are, as Ng noted, sold rather than bought, the asset manager’s role extends beyond product construction into advisory support, market insight, and the ability to articulate clearly why a particular strategy makes sense for a specific client segment.

The income trend, the fixed income constraint, and the AI overlay each reflect a different dimension of this challenge. Income strategies must be explainable and transparent. Fixed income products must clear a quantifiable hurdle. And AI-driven tools must build confidence rather than creating anxiety among advisers and clients who remain sceptical of algorithmic decision-making.

For RHB Asset Management, the path forward involves maintaining its fundamental research heritage while selectively adopting new tools that respond to demonstrable client demand. Leong’s pragmatic approach suggests a firm innovating with discipline, guided by what the market is actually asking for rather than by what the industry finds fashionable to talk about.