Bank Tellers and Related Clerks

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 6 task statements that define Bank Tellers and Related Clerks (ISCO-08 4211) score an average of 0.58 on a 0–1 exposure scale — more exposed than about 96% 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 3 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 6 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Crediting and debiting clients' accounts;”

Scores 0.64 on the 2025 scale. The task of crediting and debiting clients' accounts involves a significant amount of data entry, transaction processing, and adherence to financial regulations. This aligns closely with tasks such as handling international money transfers (score: 0.615) and executing orders for financial transactions (score: 0.62), which involve similar financial data management and processing. Generative AI can automate many aspects of this task, such as routine data processing, transaction entries, and generating reports, reducing the need for manual intervention. However, human oversight remains crucial, particularly for compliance with regulations, handling exceptions, and ensuring transactions are accurately and ethically managed. Given the context of performing this task in a high-income country like Poland, where digital literacy and infrastructure support extensive AI use, the potential for automation is significant, but not complete. Therefore, a score of 0.63 reflects the substantial capability of AI to handle routine and structured components while acknowledging the necessity of human involvement for nuanced judgment and compliance in financial operations.

Moving fastest, 2023 → 2025

“Process customer cash deposits and withdrawals, cheques, transfers, bills, credit card payments, money orders, certified cheques and other related banking transactions;”

Model capability on this task changed by −0.05 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 4211, 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.

• Tellers
• Postal Service Clerks

In context

Part of the 4 - Clerical support workers major group. Return to the full gradient to see how the whole group sits.

Bank Tellers and Related Clerks sit at the 96th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Bank Tellers and Related Clerks rank in the 96th 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 fell by 0.14 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Crediting and debiting clients' accounts;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Bank Tellers and Related Clerks". https://singulariki.com/gradient/4211-bank-tellers-and-related-clerks.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.