In the dynamic landscape of asset management, where client demands are in a constant state of flux, the traditional role of asset managers is undergoing a profound transformation. The focus is no longer solely on manufacturing funds and awaiting 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 insights shared by Edwin Leong, Head of Product Innovation and Research at RHB Asset Management, at the Malaysia Wealth Management Forum 2026. His perspective offers a glimpse into the evolving strategies and challenges faced by asset managers in a rapidly changing market.
The Shifting Market Dynamics
Leong's insights highlight two prominent trends in client capital movements. Firstly, there's a strong demand for income-oriented products, particularly those utilizing call option premium strategies to provide structured and repeatable income. This shift reflects a broader client expectation for predictability and transparency in income generation. Secondly, there's a resurgence in flows into products with full equity exposure, including technology, gold equity, and broad Asia ex-Japan strategies, as confidence in listed markets recovers.
These trends are not mutually exclusive. As one participant noted, income-driven strategies often come with trade-offs, especially regarding market beta, especially when indices are reaching 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
When it comes to fixed income, the Malaysian market is heavily skewed towards local strategies, with high-quality onshore strategies dominating. However, offshore fixed income products struggle to deliver attractive returns once currency hedging and fees are accounted for. This constraint, as Leong explained, is primarily due to the weight of institutional and government-linked capital in the market. While the institutional market is well-established, the retail and bank distribution channels are increasingly open to differentiated fixed income strategies that can deliver above-market returns.
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 implication for product designers is clear: offshore fixed income strategies 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.
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. While the firm remains rooted in traditional, fundamental stock-picking, it has embarked on a new initiative that uses AI in a more operational capacity. The mechanics are straightforward: on a monthly basis, the AI model generates allocation recommendations across asset classes, which can then be executed by portfolio managers. The key value proposition, Leong argued, is the removal of emotional bias from the allocation decision.
This 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 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. This approach not only addresses immediate client needs but also positions RHB to lead in a market where innovation and adaptability are key to success.
