This article first appeared in Wealth, The Edge Malaysia Weekly on April 27, 2026 - May 3, 2026

Xav Feng, director of LSEG Lipper Asia Pacific Research, says global funds received US$2.4 trillion in net inflows in 2025, the third-highest amount in over a decade. Malaysia drew a total of US$6.1 billion in net inflows last year, with money market and bond funds accounting for the highest numbers.

Industry players should look beyond numbers, however, and take note of the artificial intelligence (AI) trend that is fast changing the industry, from data crunching and investment research, to decision-making, client engagement, product development and more.

“The industry is at a tipping point where AI has evolved from a peripheral analytical aid into a core engine of the investment decision-making process. For global fund managers, it is no longer an experimental overlay. It is increasingly embedded in the centre of research, portfolio construction and risk management workflows,” Feng says.

As LSEG is one of the world’s largest financial market data providers, its clients have moved well beyond basic AI applications such as sentiment analysis or keyword scanning, he adds. They now deploy large language models to consume and synthesise vast amounts of non-traditional and alternative data.

“Critically, AI is automating the ‘heavy lifting’ of data aggregation, cleansing and interpretation. This allows portfolio managers and analysts to redirect their attention towards high-value activities, such as framing investment theses, assessing regime changes and determining optimal strategic and tactical positioning.”

Feng says fund houses are also using AI to transform risk management and scenario analysis. It is rapidly replacing traditional approaches, which often rely on static, backward-looking monthly or quarterly stress reports.

“Firms are now using AI-driven systems to run thousands of dynamic ‘what-if’ scenarios on a daily basis. These simulations can immediately factor in unexpected geopolitical developments, abrupt shifts in monetary policy expectations or sudden liquidity shocks. This enables faster and more informed decision-making in volatile markets,” he explains.

Together, these developments underscore a broader industry revolution. “AI is no longer simply enhancing investment processes. It is reshaping how investment insight is generated, risks are managed and capital is ultimately allocated.”

AI is also ushering in a new generation of robo-advisers that are more flexible, personalised and transparent, observes Feng.

He says the first generation of robo-advisers that emerged during the 2008 Global Financial Crisis were essentially sophisticated automated portfolio rebalancers. These platforms relied on rigid, rules-based algorithms involving periodic portfolio rebalancing, static risk profiles and model portfolios, largely ignoring the psychological dimensions of investing.

While effective in calm markets, they lacked the empathy and adaptability required to guide clients through periods of stress, volatility or life-changing events, often leading to disengagement when reassurance mattered most.

The value proposition of these earlier generations of robo-advisers lay more in cost reduction than personalisation, says Feng.

With AI capabilities, the newer generation of robo-advisers works differently from their predecessors. They can help solution providers build a far richer and more holistic understanding of a retail client by analysing spending and cash-flow patterns, stated life objectives, risk tolerance and behavioural signals over time.

In more advanced implementations, AI can even interpret how clients communicate — detecting stress, uncertainty or lack of confidence through language patterns or tone — allowing recommendations to be delivered in a way that aligns with the client’s emotional state as well as financial goals.

Crucially, Feng says, generative AI (Gen AI) addresses one of the most fundamental shortcomings of early-version robo-advisers — opacity.

“The first wave of algorithms functioned largely as ‘black boxes’, offering portfolio adjustments without explanation. In contrast, Gen AI systems are inherently explainable. They can articulate why a recommendation is being made, linking market conditions, portfolio positioning and personal circumstances in clear, plain language. This transparency helps establish credibility and trust, bringing digital advice closer to the experience traditionally associated with a private banker or wealth manager.”

Feng is increasingly seeing the emergence of a “cyborg” advisory model, which blends machine intelligence with human judgement. In this model, AI manages data-intensive tasks such as portfolio analytics, performance monitoring, tax optimisation and routine client interactions, with speed, consistency and scale.

Human advisers, freed from the administrative burden, can focus their time and expertise on areas where technology still falls short, such as complex decision-making, emotional reassurance during market drawdowns, intergenerational wealth transfer and nuanced legacy or estate planning discussions.

“Rather than replacing advisers, AI is redefining their role. The adviser becomes less of a portfolio technician and more of a trusted counsellor,” Feng says.

In the face of rapid technological advancements, asset management firms are left with little choice but to adapt. The risk of falling behind the AI technology curve is substantial, says Feng.

“The stakes are existential. Over the next three to five years, the asset management and wealth industry is likely to experience a widening and persistent ‘alpha gap’ — separating firms that successfully embed AI into their core operating model from those that fail to adapt. This divide will not be cyclical or temporary; it will be structural.”

Feng identifies three key risks for fund houses that are slow in adopting AI: operational obsolescence; information asymmetry; and talent drain and cultural decay.

He says firms that continue to rely on manual, fragmented and labour-intensive workflows will face structurally higher cost bases. Rising compliance demands, increasing data complexity and margin compression will only exacerbate the disadvantage.

In contrast, AI-native firms are redesigning operations end-to-end by automating research, reporting, client servicing and risk monitoring. This allows them to operate at a lower marginal cost, scale faster and offer more competitive fee structures without sacrificing profitability.

“Over time, traditional firms will find themselves priced out of the market and unable to compete on either cost or speed,” he warns.

With regard to information asymmetry, Feng says markets are increasingly driven by data velocity and signal interpretation rather than raw access to information. As such, firms that do not leverage AI to parse, contextualise and synthesise massive volumes of structured and unstructured data are effectively operating with a severe handicap.

“Competing against AI-enabled peers without similar capabilities is akin to trading blindfolded. While others see markets in ‘four dimensions’ — capturing relationships across time, asset classes, sentiment and macro regimes simultaneously — traditional firms risk missing the full picture.

“This asymmetry will manifest not just in missed opportunities but in persistent underperformance and slower reaction to regime shifts and tail risks,” he says.

AI adoption has an impact on talent as well, adds Feng, as top investment professionals increasingly expect AI to be part of their toolkit. Analysts and portfolio managers want leverage — not just intellectually but also technologically.

He says firms that fail to provide advanced AI tools risk losing their highest-potential talent to competitors that can empower them with better data, faster insights and more innovative workflows — thus creating a vicious circle. Weaker AI adoption causes talent departures, departures degrade performance and deteriorating performance makes talent acquisition even harder.

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