AI in Asset Management: Key Trends, Challenges, and Opportunities

Artificial Intelligence (AI) is revolutionizing asset management, driving unprecedented levels of efficiency, precision, and personalization. According to a survey by Mercer, 91% of managers are either currently using (54%) or planning to use (37%) AI within their investment strategy or asset class research.

AI is revolutionizing asset management by enhancing decision-making, streamlining operations, and delivering personalized investment solutions. Through machine learning algorithms, natural language processing (NLP), and advanced data analytics, AI helps asset managers process vast datasets, identify investment opportunities, and optimize portfolio performance. By tailoring services to individual client needs and preferences, AI improves both efficiency and service quality. 

To understand the full impact of AI in asset management, let’s explore three critical areas: investment research, operational efficiency, and client engagement. 

AI is increasingly taking on a significant role in supporting various sub-components of portfolio management. While the complete management of portfolios by AI is not imminent, AI-driven tools are proving invaluable in areas such as investment research, portfolio construction, and reporting.

AI-driven algorithms, including Random Forests, Support Vector Machines, and Neural Networks, analyze large volumes of data to uncover investment opportunities and generate alpha.

These technologies enhance the speed and accuracy of information processing, allowing asset managers to make more informed decisions. For instance, tools like Morningstar’s Intelligence Engine can rapidly analyze data and produce detailed investment insights, enabling quicker and more effective investment research.

AI significantly improves operational efficiency by automating routine tasks and streamlining workflows. This automation results in cost reductions and improved accuracy, freeing up human resources for more strategic activities. By deploying AI in departments such as marketing, finance, and human resources, asset managers can achieve substantial gains in efficiency and productivity. 

The ability of AI to analyze and structure large amounts of qualitative data quickly and accurately allows managers to make informed decisions based on comprehensive and up-to-date information. This is crucial in a fast-paced industry where timely decision-making is essential. 

Two things that matter the most to clients today are personalization and trust, which go hand in hand. The value of personalization in client portfolios rivals that of simply having industry expertise.

Although the idea of having an artificial technology tool to help personalize a client experience may seem counterintuitive, it’s actually a way to help make the personalization process faster and more efficient, which benefits both the asset manager and the client.

Which one sounds more like a conversation worth getting involved with:

• “Hello, I am reaching out to help you with your financial goals. We have an outstanding track record of success with asset management. Let’s get started.” 

Or... 

• “Good morning. [client name]! My name is John and I’ve spent X years with [company] learning how to help people with specific financial goals like making smart investments, saving for retirement expenses, and providing security to extended family members. Based on your information, it looks like you might be at a great place to start saving even a small monthly amount for your future retirement. Can we discuss what might be the right option for you?"

The one that sounds more human and has details relevant to the client is likely to win.

This approach combines AI’s efficiency with a human touch, fostering deeper client engagement and trust.

AI is significantly transforming the asset management landscape, and the effort to integrate AI is no longer a choice—it's a vital move to keep up. There are a few key AI trends to keep an eye on as this technology continues to evolve and adapt into financial services.

AI can process vast amounts of data in real-time, making it easier to identify patterns and trends. This results in more personalized investment strategies that cater to the specific risk management traits of individual clients. 

With the rise in global economic uncertainty, risk management is becoming a top priority for asset managers. AI can help mitigate risks through advanced predictive modeling and analysis. By analyzing historical market data, AI can identify potential risks and provide insights to make informed decisions.

"Work smarter, not harder" could be the official motto of AI. And over the next few years, it’s likely to work smart enough.

Robo-advisors are making investment guidance accessible to all. Predictions indicate that by 2027, managed assets could exceed the staggering figure of $4.5 trillion. The robo-advisor sector has experienced a remarkable surge in anticipated total revenue over the previous six years. From a modest $7.1 billion in 2017, this market is set to produce nearly $110 billion in sales by 2023. This sharp increase highlights the market's escalating reliance on artificial intelligence and a push toward digital assistants for portfolio management.

According to a survey by IBM, 75% of CEOs surveyed believe the organization with the most advanced generative AI will have a competitive advantage.

The field of Generative AI, often touted as the latest crown jewel in AI applications, guarantees improved performance in executing tasks and boosting financial success odds through automatic analysis, report creation, and oversight.

Its ability to manipulate large data volumes and autonomously produce user-friendly responses has sparked the interest of top-level management. 

Why? Because generative AI has the potential to: 

• Drastically transform business value chains 

• Strategically align the company for future prospects 

• Generate value for all concerned parties 

• Leave a lasting imprint on society 

Despite the benefits, there are significant challenges and considerations with AI technology: 

• Data management and quality: Ensuring high-quality data inputs is crucial for effective AI use. Addressing issues related to "dirty data" and maintaining consistent and accurate data is essential for reliable AI-driven insights. More than half (57%) of those same CEOs surveyed by IBM are concerned about data security and 48% worry about bias or data accuracy. 

• Regulatory and ethical concerns: Navigating the regulatory landscape and ensuring ethical use of AI in investment decisions are critical. Asset managers must adhere to regulatory requirements and ethical standards to avoid potential legal and reputational risks. 

• Integration and adoption: Successful integration of AI into existing workflows requires thoughtful planning and employee buy-in. Training and education are crucial for ensuring that employees understand and leverage AI tools effectively. 

A major component of addressing these challenges is ensuring continuous education on how to properly use AI and address potential issues. Although AI expertise is still developing, regular access to learning materials is crucial for success.

As AI technology evolves, new applications and innovations will emerge. Future advancements in AI could include more sophisticated predictive analytics, enhanced real-time data processing, and advanced machine learning models that adapt to changing market conditions. Asset managers who stay ahead of these trends will be well positioned to capitalize on new opportunities and deliver superior investment performance.

While AI offers powerful tools for data analysis and decision-making, the human element remains crucial. Combining AI insights with human judgment allows for a more nuanced approach to investment strategies. Asset managers must balance the use of AI with their expertise to make well-informed decisions that align with their clients' goals and risk tolerance. 

The role of asset managers is evolving with the rise of AI. They are no longer simply responsible for selecting investments and managing portfolios; they must also be knowledgeable about AI technology and its applications in asset management. This means staying up to date on the latest developments, learning new skills, and collaborating with data scientists and other experts to fully leverage AI's potential.

As AI becomes more integrated into asset management, ethical considerations and responsible investing practices will become increasingly important. Ensuring that AI applications are transparent, fair, and aligned with ethical standards will be essential for maintaining investor trust and promoting sustainable investment practices. 

Asset managers must also consider ethical implications when using AI in investment decision-making. As more sensitive data is collected and analyzed by AI systems, it's important to have proper safeguards in place to protect client information and avoid biases in decision-making.

Morningstar leverages AI in innovative ways, significantly enhancing its research and advisory capabilities. Morningstar Intelligence Engine, for example, powers "Mo," a generative AI chatbot that provides investors with summarized, conversational insights based on Morningstar’s extensive research library. This tool helps investors navigate large volumes of information quickly and efficiently. 

Morningstar is also using AI to enhance its Morningstar Medalist ratings, combining human expertise with AI to ensure that ratings are comprehensive, reliable, and reflective of the latest market data. This approach underscores Morningstar’s commitment to using AI responsibly and effectively to support better investment outcomes. 

The integration of AI in asset management represents a transformative shift that enhances efficiency, precision, and client satisfaction. As the industry evolves, the strategic application of AI will be crucial for asset managers seeking to maintain a competitive edge and deliver superior value. Morningstar’s innovative use of AI exemplifies how technology can be harnessed to improve research, streamline operations, and personalize client interactions. 

To stay at the forefront of these advancements and leverage AI’s full potential, explore the capabilities of Morningstar Intelligence Engine. The powerful platform has capabilities that facilitate the creation of an AI application, the curation of content that powers this application, and the prompt engineering and output testing that enables firms to create an AI solution for their specific needs. Morningstar has utilized this capability to enhance its own products, and we can guide you in getting started creating your own AI application. Talk to us today.