This report examines the increasing adoption of AI and big data technologies in investment management, highlighting their transformative role in workflows and decision making. It explores risks, challenges, opportunities, and global usage patterns.
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Report Overview
The financial sector is experiencing a third wave of technological innovation, characterized by the integration of hardware, software, data storage, and connectivity. This wave is driving the sector’s growth, enabling sophisticated tools based on artificial intelligence (AI) to process vast amounts of information. These advancements are transforming workflows, product innovation, and transaction speeds within the investment industry.
The first wave of technological innovation hit the sector in 1960s and 1970s, with automated individual activities such as order processing and bill payment. The second wave, facilitated by the rise of the internet during the 1980s and 1990s, brought integration across activities and among geographically distributed firms and customers.
“Creating Value from Big Data in the Investment Management Process: A Workflow Analysis” explores how investment professionals use AI and big data tools to identify associated risks, challenges, and opportunities. This report assesses, at a granular level, the technologies that investment professionals use in their work-related activities.
The report also seeks to present the key risks, challenges, and opportunities related to the use of these technologies by investment professionals and firms. For this purpose, we used a two-pronged research method to gather information. First, we conducted a global cross-sectional survey of CFA Institute members during the last two weeks of February 2024. Next, between March and April 2024, we conducted a series of roundtable discussions with investment professionals from around the world, including C-suite executives, learning specialists, practitioners, and regulators.
This report extends our existing body of work on AI, big data, and machine learning as well as our Future of Work series. CFA Institute began examining technology adoption in the investment industry in 2019, finding minimal use of AI at that time. A 2022 study by the author highlighted the increasing relevance of digital tools such as Python in optimizing job functions. This report builds on those findings, providing an updated assessment of AI and big data usage in the industry.
Increasing AI Adoption
Investment professionals are increasingly adopting AI and big data tools to complement traditional software such as Microsoft Excel. This trend reflects growing confidence in these technologies to improve efficiency and decision-making processes. Many professionals recognize the need to develop AI and technical skills to remain relevant in their roles, with strong employer support for upskilling initiatives across the industry. AI tools are being used to automate repetitive tasks, analyze complex datasets, and enhance investment strategies, as showcased in the Automation Ahead series from the CFA Institute Research and Policy Center.
Despite these advancements, however, challenges persist. Regulatory concerns, organizational resistance, and limited understanding of AI hinder its widespread adoption. Fragmented implementations and outdated infrastructure further complicate implementation. Nevertheless, AI and big data technologies present vast opportunities for refining firm strategies, enhancing collaboration, fostering professional development, and streamlining compliance processes. The integration of advanced tools such as generative AI and Python libraries is creating synergies between traditional and emerging technologies, offering new avenues for innovation.
Regulators emphasize the need for balanced governance to ensure ethical AI use. Current frameworks reveal gaps that must be addressed as adoption grows, underscoring the importance of aligning regulatory oversight with the expanding role of AI in the investment industry.
Key Findings
- Adoption trends: Although traditional tools such as Microsoft Excel remain dominant, investment professionals are increasingly integrating AI and big data tools into their workflows.
- Skill development: More than two-thirds of survey respondents expressed a desire to develop their technical skills, including the use of AI, to stay relevant in their roles.
- Practical applications: AI tools help optimize tasks by automating repetitive processes, analyzing complex datasets, and enhancing investment strategies.
- Challenges: Adoption of AI and big data technologies faces hurdles, including regulatory concerns, a gap in technology skills, and limited understanding of these complex technologies. Fragmented implementation of AI systems within organizations and outdated infrastructure further complicate implementation.
- Opportunities: AI and big data present significant opportunities to refine firm strategies, foster professional development, enhance collaboration, and streamline compliance processes.
- Regulatory implications: Regulators emphasize the need to streamline (or harmonize) global approaches to regulating AI in order to ensure ethical use of AI technologies.