How AI is shaping asset management

Key Takeaways

• Using technology can help sustain profitability.
• Strategic planning is required for the effective use of AI.
• Challenges include data input in AI models

Artificial intelligence (AI) is revolutionizing the asset management sector, driving innovation and reshaping traditional approaches. However, the critical question is how asset managers can prepare for this shift. To understand this, let’s delve deeper into key use cases, the future of AI in the industry, and the potential challenges that may arise.

How can AI transform the asset management industry?

According to BCG, the global asset management industry bounced back in 2023, with total assets under management (AUM) growing to nearly USD 120 trillion, a 12% rise from 2022. However, asset managers continued to face challenges as investors shifted to lower-fee, passively managed funds while their own operational costs rose. Their efforts to launch new products mostly failed as investors preferred to stick to established products. Only 37% of mutual funds launched in 2013 still existed in 2023.

Historically, market appreciation has driven the industry’s revenue growth. But, with slower market growth expected, asset managers are likely to face additional pressure to sustain profitability.

As the sector’s structural challenges continue, the only solution lies in using technology. Peter Czerepak, Managing Director and Senior partner at BCG, points out that generative AI opens up tremendous potential for innovation within the asset management industry. But “achieving results will require strategic thinking and the ability to execute at scale.”

Here’s a look at how AI can impact the asset management industry:

• AI tools can analyze large data sets quickly. This enhances asset management efficiency by automating routine tasks like asset tracking, significantly reducing manual effort and the cost associated with it. BCG estimates that AI can improve operational efficiency by 10% to 15% and, in a few cases, 40% to 50%.
• AI can extract information from unstructured data sources, such as news articles, online posts, reports, and images, reports a CFA Institute Research Foundation As a result, a large amount of information can be included automatically in financial analysis, without manual intervention. This can be used to assess ESG factors and uncover investment insights.
• Portfolio Optimization AI-driven algorithms analyze massive datasets to construct optimized portfolios, balancing risk and return based on real-time market conditions.
• AI ensures data consistency and accuracy by obtaining information from asset records. It flags incomplete records or duplicate and redundant information, and suggests an appropriate action.
• AI tools used by asset managers offer predictive insights helping them in decision-making. For example, they can predict investor volatility and appropriate preventive measures. AI also improves risk management by detecting potential risks and fraud in real time, which is vital to avoid financial losses.
• Unlike other statistical techniques, AI algorithms can adjust themselves based on data, according to the CFA This eliminates the need for manual reconfiguration or parameter re-estimation required by traditional models.

The future of AI in asset management

AI will continue to transform the asset management sector in the coming years.

The sector is likely to see the emergence of more powerful machine-learning (ML) models capable of processing larger datasets faster and more accurately, enabling managers to make more precise forecasts.

A key development could be the emergence of fully automated investment platforms that manage portfolios with minimal human input and offer personalized investment plans. “It could help create portfolios that automatically adjust to changing market conditions,” according to a Pragmatic Coder report.

However, advancements in AI are likely to draw increased regulatory scrutiny, prompting new standards to ensure its ethical use and transparency.

What can go wrong?

Despite all the buzz around how AI can be the next big thing, there are few risks involved with AI that can turn catastrophic.

• If incorrect data is inserted into the AI model, it can make wrong decisions, resulting in huge losses. Mike Pilch, Grant Thornton’s managing director for Transformation, points out that “You need control over the data that’s being used to make sure AI, chat bots, and other solutions are providing real insight based on facts and legitimate, trustworthy sources.”
• A CFA Institute Research Foundation study reveals that “Understanding and explaining the inferences made by most AI models is difficult, if not impossible.” As AI models grow in complexity, inferences become increasingly opaque, making human supervision challenging and often ineffective.
• AI models’ unpredictability during “black swan” events and their tendency to make simultaneous errors can lead to market crashes. Owing to the considerable cost of producing AI algorithms, most asset managers use similar tools, increasing the risk of such crashes.
• Noting that AI models often require vast amounts of data for training, which may not be available, the CFA report says that the lack of data might lead to improper calibration due to poor signal-to-noise ratios—especially in low-frequency financial data with many missing observations.
• Integrating AI into asset management requires both financial and technical expertise. However, many finance professionals lack AI skills, while data scientists may not grasp the nuances of financial markets, leading to a skill gap.

Can AI replace people?

Despite its vast usage, AI can never replace humans. AI excels at data and performs complex analyses, but it lacks human intuition. Investors are still skeptical about AI’s ability to make unbiased decisions. Hence, while financial advisors will use AI tools to enhance efficiency, they are unlikely to rely on them fully.

However, AI tools can be perceived as an enabler as they enhance efficiency without major restructuring, Grant Thornton’s Pilch points out. “Using technology smartly offers quick returns on data sets, enabling organizations to achieve their transformation goals more effectively.”

The momentum is with AI as the use cases become more apparent with each passing day, but market participants must still carefully weigh the benefits of the technology against the heavy investment required. For many, it is not a journey they will want to embark on themselves as they also seek to improve efficiencies internally through better front-to-back integration and automation. Having a trusted technology partner aboard to guide the process is a prerequisite to business success.