How Is AI Used in Investment Management?

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    Managing investments remains a very complex aspect of financial services. With so many factors having an impact, investment decisions must be fast and accurate to ensure a positive outcome. The volatility of the market itself affects the ability to implement changes to investment strategies. That is where having the necessary data available is key to the decision-making process. Leveraging and analyzing data to drive better outcomes is now possible to augment with artificial intelligence (AI). In this post, you’ll learn how AI can be applied to investment management.

    Artificial Intelligence in Investment Management

    Adopting AI as part of your investment management process can offer benefits. AI for investment has much promise and is somewhat at the beginning phases of adoption from an industry standpoint. Let’s get familiar with the framework.

    What Is AI in Investment Management?

    First, let’s define AI investment management. AI technology can make use of available investment data to enhance outcomes when it comes to analyzing data used in the decision-making process.  The goal is to analyze data faster and with the potential of gaining greater insights than the typical process where teams of analysts sift through data manually to reach the same results.  

    AI can play a role in making sense of large volumes of data more rapidly to create insights in an automated fashion

    Delivery of AI functionality should be applied within a firm’s investment management software. AI data analytics can be thought of as an additional module that can be used to deliver insights using such AI-based approaches as natural language processing (NLP) and machine learning (ML).

    The State of Artificial Intelligence Investment Management

    There is significant value in adopting AI-based tools Adoption has been somewhat slow in this segment of the market, even with 82% of wealth management firms prioritizing AI initiatives. It’s worth noting that the market is growing, expected to reach $13.4 billion by 2027. The speed of adopting AI technology likely stems from the industry getting up to speed on the subject of AI itself and also on analyzing use cases among peers.

    The reality is that AI-based approaches within investment management have the potential to reduce costs and even have a multiplier effect for firms using them. The goal is to have AI work more as a supplement to human intelligence. Additionally, more investment professionals are pursuing AI skills, with 64% stating they are developing these capabilities. This is especially true for the next generation of industry professionals within the investment management space.

    There’s also concern about the inherent risks related to AI. These fall into a few different categories, including:

    • Data integrity and data privacy issues depend on the sourcing and processing of data used in large language models that power AI
    • The possibility of “hallucinations” in the case of generative AI based on spurious conclusions drawn from the data models of leading providers like Chat GPT
    • The likelihood of future regulations on AI-created content
    • Cybersecurity concerns around opening up a firm’s own data to be used for the purposes of AI

    Investment firms need to consider these areas when developing their own AI framework and choosing the right investment management technology.

    How Does Investment Management Technology Apply AI, and What Are the Benefits?

    There are a number of ways to apply AI technology to the investment management process. Below are some use cases and the benefits that they deliver:

    • Aggregating more data into models used by AI applications: Adding more data leads to better outcomes, but cleansing and standardizing data is a monumental task. AI can often do this, saving time and enriching data sets used within investment management.
    • Searching data using AI tools such as NLP (natural language processing).  Data sets that are empowered by AI can often utilize NLP to more effectively interrogate data with faster results.  Information is power and having data at one’s fingertips is the goal of utilizing AI to screen through large and disparate sets of data. 
    • Enhanced personalization: AI can also support customers, streamlining their journey by delivering the most relevant information based on past and present interactions. It can also alert stakeholders as to specific interactions by clients so client services professionals can more proactively respond to client inquiries.
    • More Effective Compliance Monitoring: AI is an effective way to monitor transactions, whether looking for suspicious or abnormal activity or screening of personal trading activity of employees involved with the investment management of the firm. The technology will flag those transactions requiring further human review to determine if these are problematic and require actions.
    • Optimizing Portfolio Management and Trading Processes: OMS and Portfolio Management systems that are empowered by AI can optimize workflows and eliminate complication in the investment decision making and trading process.  NLP is a great way to do this.

    In addition to using AI in investment management, it can also be an effective tool for wealth management.  

    Artificial Intelligence in Wealth Management

    Of AI’s capabilities, the most crucial in wealth management is personalization. Artificial intelligence in wealth management empowers advisors to offer more customized customer service interactions. Machine learning is the central component for this purpose.

    Providing data and personalization is becoming a requirement for investors. A study predicted that by 2030, 80% of new wealth management clients will demand this. It will no longer be a differentiator that you have the technology to deliver this capability; it will be necessary for the operating model of most wealth management firms.

    Along with personalized financial advice, AI in wealth management has other benefits, such as:

    • Analysis of data to locate market trends and investment opportunities
    • Automation of manual tasks that are repeatable and need little human intervention, which boosts efficiency
    • Streamlined compliance monitoring
    • Data-driven decision-making with greater access to insights

    Investment managers and wealth managers of all types have much to gain from AI, and the future indicates that more innovation is on the way.

    The Future of AI Investment Management

    Emerging  AI trends will shape the future of investment management. They have the potential to affect additional workflows and interactions, including:

    • Analyzing Investors Documentation: OpenAI using NLP currently can dialogue with investment analysts at a more advanced level than standardized chatbots. Those capabilities will only expand as more data becomes available for large language models. Reshaping operations: AI is likely to change how everyone works and how it can be applied to drive practical outcomes with greater efficiencies and savings.
    • More sophistication within investment strategies: As AI-based algorithms consume more financial data and market trends, greater personalization of portfolios according to individual client needs is possible. Customized and optimized portfolios will become the norm.

    AI in Investment Management from INDATA

    With all of the current and potential use cases for artificial intelligence within investment management, there is much for investment management firms to gain. Combining the abilities of AI with human intelligence can yield better outcomes for the industry as a whole. See how we are integrating AI into our investment management software by requesting a demo.