# Gambling Cage Workers

> In a gambling establishment, conduct financial transactions for patrons. Accept patron's credit application and verify credit references to provide check-cashing authorization or to establish house credit accounts. May reconcile daily summaries of transactions to balance books. May sell gambling chips, tokens, or tickets to patrons, or to other workers for resale to patrons. May convert gambling chips, tokens, or tickets to currency upon patron's request. May use a cash register or computer to record transaction.

- **SOC code:** 43-3041.00
- **Canonical URL:** https://singulariki.com/roles/role-43-3041-00
- **Also known as:** Cage Cashier, Casino Cage Cashier, Casino Cashier, Vault Cashier, Cage and Players Club Rep (Cage and Players Club Representative), Casino Services Rep (Casino Services Representative), Dual Rate Banker, Gaming Cage Worker
- **Frame:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- Maintain confidentiality of customers' transactions.
- Follow all gaming regulations.
- Prepare bank deposits, balancing assigned funds as necessary.
- Maintain cage security.
- Cash checks and process credit card advances for patrons.
- Supply currency, coins, chips, or gaming checks to other departments as needed.
- Prepare reports, including assignment of company funds or recording of department revenues.
- Convert gaming checks, coupons, tokens, or coins to currency for gaming patrons.
- Record casino exchange transactions, using cash registers.
- Count funds and reconcile daily summaries of transactions to balance books.
- Verify accuracy of reports, such as authorization forms, transaction reconciliations, or exchange summary reports.
- Determine cash requirements for windows and order all necessary currency, coins, or chips.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Customer and Personal Service _(knowledge)_
- Oral Comprehension _(ability)_
- Oral Expression _(ability)_
- Near Vision _(ability)_
- Mathematics _(knowledge)_
- Speech Clarity _(ability)_
- Problem Sensitivity _(ability)_
- Mathematical Reasoning _(ability)_
- Number Facility _(ability)_
- Speaking _(essential_skill)_
- Active Listening _(essential_skill)_
- Mathematics _(essential_skill)_

**Skills in demand:**
- Mathematics _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Information Ordering _(Specialized Skill)_
- Deductive Reasoning _(Common Skill)_
- Active Listening _(Common Skill)_
- English Language _(Common Skill)_
- Writing _(Common Skill)_
- Time Management _(Common Skill)_
- Social Perceptiveness _(Common Skill)_
- Reading Comprehension _(Common Skill)_
- Microsoft Word _(Common Skill)_
- Microsoft PowerPoint _(Common Skill)_

**Tools & technology:**
- Microsoft Excel _(hot technology)_
- Microsoft Office software _(hot technology)_
- Microsoft Outlook _(hot technology)_
- Microsoft PowerPoint _(hot technology)_
- Microsoft Word _(hot technology)_
- Corel WordPerfect Office Suite

## AI exposure & outlook

- **AI task-overlap index:** 56th percentile (Moderate) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 58th percentile (Moderate) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 41st percentile (Moderate) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 73rd percentile (High) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 43rd percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** -5.0% growth (Declining); 1.3k annual openings; 14.1k → 13.4k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $36,990; 13,490 employed.

## How people actually use AI here

Anthropic Economic Index — measured AI conversations mapped to this occupation's tasks:

- **Automation vs augmentation:** 45% automation, 40% augmentation (usage-weighted).
- **Autonomy median:** 3.8 (higher = AI acts more independently).
- **Dominant collaboration mode:** directive.

**Tasks most handed to AI here:**
- Provide customers with information about casino operations. _(1.2% of measured AI use; directive)_
- Prepare reports, including assignment of company funds or recording of department revenues. _(0.6% of measured AI use; directive)_

**Example prompts (honest phrasings of the tasks above — starting points, not endorsed instructions):**
- Help me provide customers with information about casino operations.
- Help me prepare reports, including assignment of company funds or recording of department revenues.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Anthropic Economic Index** (v4 (2026-01-15) + v2 (2025-03-27)) — Anthropic. https://www.anthropic.com/economic-index
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Frey & Osborne (2013)** (frey-osborne-automation) — academic. https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment/
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

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_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-43-3041-00_