# Retail Loss Prevention Specialists

> Implement procedures and systems to prevent merchandise loss. Conduct audits and investigations of employee activity. May assist in developing policies, procedures, and systems for safeguarding assets.

- **SOC code:** 33-9099.02
- **Canonical URL:** https://singulariki.com/roles/role-33-9099-02
- **Also known as:** Loss Prevention Agent, Loss Prevention Associate (LPA), Loss Prevention Detective, Loss Prevention Officer, Asset Protection Associate (APA), Loss Prevention Investigator, Loss Prevention Specialist, Retail Asset Protection Specialist
- **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):
- Investigate known or suspected internal theft, external theft, or vendor fraud.
- Implement or monitor processes to reduce property or financial losses.
- Identify and report merchandise or stock shortages.
- Maintain documentation or reports on security-related incidents or investigations.
- Apprehend shoplifters in accordance with guidelines.
- Verify proper functioning of physical security systems, such as closed-circuit televisions, alarms, sensor tag systems, or locks.
- Identify and report safety concerns to maintain a safe shopping and working environment.
- Conduct store audits to identify problem areas or procedural deficiencies.
- Monitor compliance with standard operating procedures for loss prevention, physical security, or risk management.
- Inspect buildings, equipment, or access points to determine security risks.
- Perform covert surveillance of areas susceptible to loss, such loading docks, distribution centers, or warehouses.
- Prepare written reports on investigations.

**Emerging tasks** (O*NET):
- Use drone technology for surveillance and loss prevention.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Public Safety and Security _(knowledge)_
- English Language _(knowledge)_
- Law and Government _(knowledge)_
- Oral Comprehension _(ability)_
- Problem Sensitivity _(ability)_
- Critical Thinking _(essential_skill)_
- Oral Expression _(ability)_
- Deductive Reasoning _(ability)_
- Customer and Personal Service _(knowledge)_
- Education and Training _(knowledge)_
- Inductive Reasoning _(ability)_
- Speech Recognition _(ability)_

**Skills in demand:**
- English Language _(Common Skill)_
- Deductive Reasoning _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Inductive Reasoning _(Common Skill)_
- Active Listening _(Common Skill)_
- Social Perceptiveness _(Common Skill)_
- Reading Comprehension _(Common Skill)_
- Information Ordering _(Specialized Skill)_
- Active Learning _(Common Skill)_
- Writing _(Common Skill)_
- Time Management _(Common Skill)_

**Tools & technology:**
- Microsoft Access _(hot technology)_
- Microsoft Excel _(hot technology)_
- Microsoft Office software _(hot technology)_
- Microsoft Outlook _(hot technology)_
- Microsoft PowerPoint _(hot technology)_
- Microsoft Windows _(hot technology)_
- Microsoft Word _(hot technology)_
- Structured query language SQL _(hot technology)_
- Aspect Loss Prevention Aspect EliteLP
- Case management system software
- Database software
- Enterprise application integration EAI software

## AI exposure & outlook

- **AI task-overlap index:** 36th percentile (Moderate) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 50th percentile (Moderate) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 54th percentile (Moderate) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 10th percentile (Low) — source: microsoft_applicability.
- **Remote-capable (Dingel–Neiman):** yes — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 2.5% growth (About average); 23.3k annual openings; 84k → 86.1k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $41,600; 83,110 employed.

## How people actually use AI here

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

- **Automation vs augmentation:** 28% automation, 36% augmentation (usage-weighted).
- **Autonomy median:** 3.5 (higher = AI acts more independently).
- **Dominant collaboration mode:** task iteration.

**Tasks most handed to AI here:**
- Prepare written reports on investigations. _(0.9% of measured AI use; task iteration)_
- Identify and report safety concerns to maintain a safe shopping and working environment. _(0.4% of measured AI use; none)_

**Example prompts (honest phrasings of the tasks above — starting points, not endorsed instructions):**
- Help me prepare written reports on investigations.
- Help me identify and report safety concerns to maintain a safe shopping and working environment.

## 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
- **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-33-9099-02_