In the dynamic landscape of asset management, where client demands are in a constant state of flux, the traditional roles of asset managers are evolving. The focus is no longer solely on manufacturing funds and waiting for distribution; instead, it's about actively reading market trends, identifying gaps in advisory portfolios, and leveraging emerging technologies like artificial intelligence (AI) to enhance investment decision-making. This shift is particularly evident in the Malaysia Wealth Management Forum 2026, where industry leaders discussed the latest trends in portfolio construction, product sourcing, and scaling advice across various client segments. Among the panellists, Edwin Leong, Head of Product Innovation and Research at RHB Asset Management, offered a unique perspective from the viewpoint of a product originator. His insights shed light on the evolving preferences of client flows, the challenges and opportunities in Malaysia's fixed income market, and the innovative use of AI in asset allocation.
Following the Flows
One of the key takeaways from the forum was the dominance of income-oriented strategies in client flows. Leong highlighted that there is a strong demand for products that use call option premium strategies to deliver structured and repeatable income. This trend reflects a broader shift in client expectations towards predictability and transparency in income generation. Beyond income, there is also a return of flows into products with full equity exposure, particularly in technology, gold equity, and broad Asia ex-Japan strategies. This trend indicates a growing confidence in listed markets, with clients seeking a balance between stable cash generation and participation in equity upside.
The Fixed Income Constraint
When it comes to fixed income, Leong noted that Malaysia's market is heavily skewed towards local strategies, dominated by institutional and government-linked capital. However, he also highlighted the growing appetite for differentiated fixed income strategies in retail and bank distribution channels. The critical constraint remains the hedging cost, which makes offshore fixed income strategies less viable for Malaysian investors. Any offshore strategy must clear the hurdle of currency hedging and associated fees before it can offer genuine value over local alternatives. This constraint has significant implications for product designers, as offshore fixed income strategies may not survive the translation into ringgit-denominated returns.
AI as an Allocation Tool
Leong's most forward-looking contribution concerned RHB Asset Management's adoption of AI as a tool for asset allocation. The firm has launched a strategy that uses an AI overlay to determine monthly asset allocation, removing emotional bias from the investment process. This approach sits at the pragmatic end of the AI spectrum, where 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 are still in the early stages of AI adoption, RHB's approach offers a useful case study, demonstrating the potential benefits of targeted AI applications.
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 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. For RHB Asset Management, the path forward involves maintaining its fundamental research heritage while selectively adopting new tools that respond to demonstrable client demand, as exemplified by Leong's pragmatic approach.
