Financial Analysts

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 9 task statements that define Financial Analysts (ISCO-08 2413) score an average of 0.62 on a 0–1 exposure scale — more exposed than about 98% of the 427 placed occupations. Roughly 100% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Gradient 4 band.

Exposure is task overlap, not a verdict. A high score means a generative-AI model can do part of the content of these tasks — it says nothing about whether the work is automated, whether anyone uses AI for it today, or whether jobs are lost. The gradient is scored on the international ISCO-08 system; the rest of Singulariki is U.S. O*NET/SOC, bridged below by an approximate, many-to-many crosswalk.

How its tasks split across the gradient

Each of the 9 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Presenting oral and written reports on general economic trends, individual corporations, and entire industries.”

Scores 0.68 on the 2025 scale. The task of presenting oral and written reports on economic trends, corporate performance, and industry analysis aligns with several aspects of tasks rated in the context. It involves significant data analysis, trend identification, and report drafting, where Generative AI can significantly aid by automating data processing, generating drafts, and even conducting preliminary analyses. Such capabilities align the task with others that involve data handling and report preparation, such as "Preparing reports and expert opinions" with an automation score of 0.65. These tasks benefit from AI's ability to process large datasets, generate reports, and identify trends, reducing the manual workload. However, like the tasks mentioned, strategic interpretation and personalized presentation tailored to a specific audience require human oversight, ensuring accuracy, context, and nuanced judgment. In a high-income country like Poland, with extensive technological access, these tasks can leverage AI for substantial automation with human oversight where detailed strategic understanding and contextual adaptation are necessary. Therefore, the score reflects substantial potential for automation, considering AI's role in automating routine and data-driven components of the task while retaining the need for human participation in interpretation and presentation.

Moving fastest, 2023 → 2025

“Determining the prices at which securities should be syndicated and offered to the public;”

Model capability on this task changed by +0.29 in two years — the gradient is not static, it is filling in.

U.S. occupations this maps to

The American O*NET/SOC roles that crosswalk to ISCO-08 2413, biggest by employment first, via the published (approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 correspondence. These are the closest U.S. matches — not an asserted one-to-one identity.

• Financial and Investment Analysts
• Credit Analysts
• Financial Examiners

In context

Part of the 2 - Professionals major group. Return to the full gradient to see how the whole group sits.

Financial Analysts sit at the 98th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Financial Analysts rank in the 98th percentile for GenAI task exposure — overlap with what generative AI can attempt, not a projection of displacement. (ILO / Gmyrek et al. (2025) GenAI exposure gradient)
• About 100% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure rose by 0.02 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Presenting oral and written reports on general economic trends, individual corporations, and entire industries.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Financial Analysts". https://singulariki.com/gradient/2413-financial-analysts.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.