The Evolution of AI in Asset Management

Artificial intelligence (AI) in asset management has steadily grown over the past several years as even formerly slow adopters are seeing the value and necessity in using it. Today, financial services professionals, historically notorious for resisting AI in their work, are finally realizing that AI tools don’t replace human intelligence—they enhance it.

By integrating AI tools and technologies into asset management, firms and fund managers can eliminate manual, repetitive tasks and focus on strategic, alpha-generating priorities.

AI enables analysts to uncover opportunities even amidst volatility, operate with higher agility, perform data analysis at scale, and assess risk more accurately. As time goes on and more firms adopt AI tools, these capabilities will become even more essential to maintain a competitive advantage.

Research shows that the AI in asset management market will grow at a staggering 34% CAGR through 2027. The time is now to invest in a market intelligence platform with AI search capabilities, so you can speed up your research and outperform the market. 

Asset Management Is Changing: The Journey of AI

The asset management industry is constantly evolving with increased AI integration. Big data, high levels of automation, and an increasingly rapid pace of business have demanded that asset managers and other financial professionals in this space adopt AI tools to stay relevant.

AI has become more prevalent in every part of asset management, from opportunity identification to due diligence to investment decision-making to performance analysis to ongoing portfolio management and more.

It’s becoming evident that those who become adept at using these tools will stay a step ahead, while those slower to adopt (or who refuse altogether) will be hard-pressed to keep up.

Let’s look at some of the most timely trends and use cases for AI in asset management now and in the near future.

AI in Asset Management: Key Trends

AI is Becoming the Cornerstone of Investment Research

Traditional (read: manual) investment research processes are becoming impossible to execute at the scale required by big data and fast-moving markets. Asset management firms need a way to transform their processes to be highly automated and intelligent—in other words, they need to get faster without sacrificing quality.

Artificial intelligence is the only way to do it. AI-based market intelligence platforms enable you to make smarter investment decisions at scale with key capabilities like:

  • Top-Tier Data Sources: Access to content and datasets not easily found on search engines, social media, or other public sources—including regulatory filings, broker research, private company data, expert call transcripts, and premium news.
  • Sentiment Analysis:  AI tools using Natural Language Processing (NLP) to extract sentiment from documents allow you to assess more nuanced meaning behind text.
  • Dashboards and Visuals: Dashboard views and visual charts, graphs, and tables make presentations more approachable and quickly readable to a wider audience.
  • Intelligent Search: Algorithms and other AI technologies make every search smarter. The tool can recognize your intent and deliver highly relevant results.
  • Automated Alerts: Automation delivers you highly customized alerts so you never miss an update about companies, industries, or topics you care about.
  • Predictive Data Analytics: AI algorithms can deliver predictive insights to power accurate forecasts and smarter decisions for the future.

Related Reading: How AI is Shaping the Future of Investment Research

Robo-Advisors Enable Customization at Scale

Robo-advisors are on the rise, utilizing AI algorithms to provide automated and personalized investment advice. Revenue generated by robo-advisors has multiplied by 15X between 2017 and 2023, and it’s expected to continue on this trajectory for the foreseeable future.

The advantage of robo-advisors is that asset management firms can scale their services to previously underserved customer segments in this space, for whom a traditional human advisor may have been out of budget or otherwise inaccessible.

The challenge for firms will be balancing the availability of robo-advisors with demonstrating the value of their human advisors’ perspectives. This doesn’t pose quite the same challenge as it does in other industries, however. Investors trust and want to work with human advisors, and though they may incorporate robo-advisors in their workflow, they are unlikely to fully make the switch. 

Still, it’s critical to keep an eye on this trend, as human advisors are expected to have access to the same level of AI insights as their robo competitors.

Quantamental Insights Gain Momentum

Quantamental insights combine machine learning and AI with human knowledge and experience. For asset managers, it’s the ideal scenario for adding AI to the investment decision process without sacrificing quality or the unique human perspective that can only be provided by a specific person or team.

In practice, quantamental research for asset management means using AI to mine and analyze massive amounts of data for insight, while also depending on the knowledge of particular companies, expert commentary, and other anecdotal insights that are essential for informing decisions.

Risk Management and Fraud Detection

AI-powered tools are significantly better at risk management because they can identify potential risks and anomalies in real-time. Machine learning algorithms can detect irregular trading patterns, market disruptions, and other factors that might pose risks to investments long before a human would notice. Similarly, AI helps in fraud detection by spotting unusual activities that might indicate fraudulent behavior.

This level of vigilance contributes to maintaining market integrity and investor confidence, and this will be an important differentiator in the future for winning and keeping client trust.

Generative AI Adoption

Generative artificial intelligence (genAI) is the most recent AI development that has taken the financial and technological worlds by storm. Now integrated into nearly every industry, genAI is fast becoming a must-have for any firm that aims to get the competitive edge.

In asset management, genAI can be used to automate tasks such as data entry, report generation, and compliance monitoring, freeing up time for higher-value strategic tasks. It can also help generate insights by identifying patterns and trends that could be difficult to spot for a human—enabling better investment decisions and more effective risk management. 

Learn more about AlphaSense’s generative AI feature, Smart Summaries, purpose-built for business and financial professionals.

Regulatory and Ethical Considerations

The increased reliance on AI has unsurprisingly raised regulatory and ethical questions. As AI systems play a larger role in investment decisions, transparency and explainability become crucial. Regulators require asset managers to justify the use of AI models and ensure that they do not introduce biases or manipulate markets.

Striking a balance between innovation and adherence to regulations is an ongoing priority that asset managers must stay attuned to.

Talent and Skill Shifts

The integration of AI in asset management has led to a shift in required skills. Asset managers now need to be well-versed in data science, machine learning, and AI techniques to effectively develop and implement AI-driven investment strategies.

This shift in skill requirements has prompted collaborations between financial professionals and data scientists, fostering interdisciplinary expertise. Further, firms that win will be those whose analysts and asset managers buy into AI and are willing to be trained to use it effectively.

The AI Advantage

It’s clear that AI is here to stay, and the time is now for firms to get on board with this technology. Adopting AI in asset management sooner rather than later positions firms competitively in a market that will only become more technology-driven in the future.

But concerns about balancing human and artificial intelligence are real and especially pertinent in industries where financial decisions are being made. That’s why the AlphaSense platform was uniquely designed to provide asset managers with best-of-both-worlds capabilities.

On the platform, you’ll have access to cutting-edge AI capabilities, as well as a wealth of human knowledge in the form of broker research, expert calls, and more.

Qualitative analysis features like sentiment analysis and Smart Synonyms™ maintain the meaning behind results you find on AlphaSense. At the same time, capabilities like generative AI, automated alerts, and sophisticated algorithms accelerate the speed at which you do research and drive optimization of key workflows.

Key areas of your investment research process you can level up with AlphaSense include:

  • Thesis Validation: Search across and review multiple financial content sources in one place and easily extract quantitative data from qualitative sources.
  • Top-Down Research: Leave CTRL+F behind and conduct thematic searches across broker research, transcripts, SEC filings and moreall in one place.
  • Bottom-Up Research: Easily find the “why” behind a company’s financials with the ability to explore how key topics are discussed over time.
  • Earnings Analysis: Identify shifts in market commentary with sentiment analysis in event transcripts.

Learn more about the AlphaSense impact by reading client success stories:

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Nicole Sheynin
Nicole Sheynin
Content Marketing Specialist

Fueled by empathy-driven storytelling and good coffee, Nicole is a content marketing specialist at AlphaSense. Previously, she has managed her own website/blog and has written guest posts for various other publications.

Read all posts written by Nicole Sheynin