Financial and Investment Analysts

Conduct quantitative analyses of information involving investment programs or financial data of public or private institutions, including valuation of businesses.

The occupation "Financial and Investment Analysts" has a base automation risk of 51.0%, reflecting a moderate likelihood of automation. This risk is driven primarily by the structured and data-driven nature of many tasks within the role. For example, tasks such as advising clients on aspects of capitalization—including determining amounts, sources, or timing—are highly data-centric and based on established models and formulas, making them suitable for algorithmic approaches. Analyzing financial or operational performance of companies facing difficulties also relies on quantitative data, trend analysis, and standardized methodologies that can be automated using artificial intelligence and big data analytics. Similarly, assessing companies for investment suitability by examining company facilities can leverage automated checklists, remote assessment tools, and data mining techniques. However, certain tasks within the role demonstrate high resistance to automation due to their inherently interpersonal, specialized, or judgment-based nature. Supervising, training, or mentoring junior team members requires soft skills, empathy, and adaptability, which remain challenging for AI to replicate. Specializing in green financial instruments—such as constructing or evaluating socially responsible mutual funds or ETFs—demands both domain expertise and ongoing engagement with evolving regulatory and ethical standards, making automation complex and context-dependent. Furthermore, recommending investments and investment timing requires expert intuition, an understanding of nuanced market factors, and persuasive communication tailored to specific clients, all of which still resist current automation technologies. The bottleneck skills that constrain automation in this occupation primarily involve higher-level soft and analytical abilities. Specifically, advanced communication (mentoring, client persuasion), complex judgment (investment recommendations under uncertainty), and domain-specific expertise (green finance specialization) are less amenable to automation. Automation risk is further mitigated by the need for situational awareness (assessing company culture, market subtleties) and adaptability to changing regulations or client preferences. While technological advances may continue to erode the purely analytical aspects of the job, these bottleneck skills—requiring high levels of expertise and human interaction—currently set a ceiling on how much of the financial analyst's work can be wholly automated.