Financial analysts

Analyze financial data using specialized software and create forecasts

AI and specialized financial software can perform complex financial modeling, run Monte Carlo simulations, create forecasts, and generate visualizations with minimal human input. Analysts primarily need to specify parameters and validate outputs rather than perform calculations manually.

BLS evidence: Financial analysts must be adept at using software to analyze financial data and trends, create portfolios, and make forecasts.

Prepare written reports on investment recommendations and strategies

AI can generate well-structured investment reports from data and analysis, incorporating charts, tables, and narrative explanations. Current LLMs produce professional-quality financial writing. Human review ensures accuracy and appropriate tone, but the drafting labor is largely automated.

BLS evidence: Financial analysts prepare written reports and are expected to explain investment decisions and strategies in meetings with stakeholders.

Examine company financial statements to determine business value

AI can parse financial statements, calculate ratios, identify red flags, and benchmark against peers with high accuracy. Modern LLMs can interpret footnotes and MD&A sections. Human review remains valuable for detecting sophisticated accounting manipulation and contextual judgment.

BLS evidence: Financial analysts examine a company's financial statements to determine its value.

Evaluate current and historical financial data to identify investment opportunities

AI systems excel at processing historical financial data, identifying patterns, and flagging opportunities using quantitative models. Current LLMs with data analysis capabilities can perform this task with human review of flagged opportunities, though judgment on non-quantifiable factors still benefits from human oversight.

BLS evidence: Financial analysts evaluate opportunities to commit money for the purpose of generating profit and must evaluate current and historical financial data.

Evaluate financial risks and determine strategies to limit losses

AI excels at quantitative risk modeling, scenario analysis, stress testing, and identifying hedging strategies. Modern systems can evaluate portfolio risk across multiple dimensions and suggest mitigation approaches. Human oversight is needed for tail risks and strategy selection in novel situations.

BLS evidence: Financial risk specialists evaluate threats to investment decisions and determine how to manage unpredictability and limit potential losses.

Study economic and business trends affecting investments

AI can ingest vast amounts of economic data, news, and research reports to identify trends and correlations. LLMs can synthesize trend analysis from multiple sources. However, distinguishing signal from noise in unprecedented situations and evaluating second-order effects still benefits from human expertise.

BLS evidence: Financial analysts study economic and business trends and must understand how economic trends, new regulations, policies, and political situations may affect investments.

Select mix of products, industries, and regions for investment portfolios

AI can optimize portfolio allocation based on risk-return parameters, correlation matrices, and constraints using sophisticated algorithms. However, incorporating qualitative factors, client-specific circumstances, and making final allocation decisions in uncertain environments still requires human judgment and accountability.

BLS evidence: Portfolio managers select the mix of products, industries, and regions for their company's investment portfolio and are responsible for the overall performance.

Recommend individual investments and portfolios to clients or companies

AI can generate investment recommendations based on risk profiles and financial data, but the client relationship aspect, understanding unstated preferences, and liability for advice require substantial human involvement. Robo-advisors handle simple cases, but complex client situations need human judgment.

BLS evidence: Financial analysts recommend individual investments and collections of investments, known as portfolios.

Make buy or sell decisions in response to market conditions

While algorithmic trading systems make automated buy/sell decisions at high frequency, discretionary decisions in response to complex market conditions involve judgment about unprecedented events, market psychology, and risk tolerance that humans remain responsible for, especially for client accounts.

BLS evidence: Fund managers and portfolio managers frequently make buy or sell decisions in reaction to quickly changing market conditions.

Assess management team strength and capabilities

While AI can analyze track records, credentials, and public statements of management teams, assessing intangible qualities like integrity, adaptability, and leadership under pressure requires human judgment from interviews and observation. AI can provide supporting analysis but cannot make the holistic assessment.

BLS evidence: Financial analysts assess the strength of the management team.

Meet with company officials to gain insight into prospects

This task requires physical presence, reading body language, building trust relationships, asking adaptive follow-up questions in real-time, and extracting information executives may be reluctant to share. AI cannot replicate the interpersonal dynamics and on-site observation critical to these meetings.

BLS evidence: Financial analysts meet with company officials to gain better insight into the company's prospects.