Key Takeaways
- Employee theft comprised 29.8% of U.S. shrinkage in 2023
- U.S. retailers lost $15.6 billion to employee theft in 2023
- Sweethearting (employee discounts abuse) 10% of employee theft U.S. 2023
- U.S. retailers recovered $21.1 billion from shrinkage in 2023 (53% rate)
- Apprehensions recovered $1,500 avg per case U.S. 2023
- Prosecution value avg $9,000 per case U.S. 2023
- RF technology adoption 75% vs ORC U.S. retailers 2023
- AI video analytics reduced shrinkage 25% in 200 U.S. stores 2023 pilot
- EAS systems deterred 68% shoplifting attempts U.S. 2023
- Organized Retail Crime (ORC) caused 4.5% of U.S. shrinkage in 2023
- U.S. ORC losses exceeded $5 billion annually in 2023
- 92% U.S. retailers affected by ORC in past year 2023
- In 2023, U.S. retailers experienced a total inventory shrinkage of 1.65% of annual sales, amounting to $112.1 billion in losses
- Globally, retail shrinkage averaged 1.75% of sales in 2022, with apparel sector at 2.1%
- U.S. grocery shrinkage rate reached 3.0% in 2022, highest among sectors due to high-value items
In 2023, employee theft cost US retailers $15.6 billion, with audits and hunches catching most cases.
Employee Theft
Employee Theft Interpretation
Financial Impact and Recovery
Financial Impact and Recovery Interpretation
Loss Prevention Technologies
Loss Prevention Technologies Interpretation
Organized Retail Crime
Organized Retail Crime Interpretation
Overall Shrinkage Statistics
Overall Shrinkage Statistics Interpretation
Shoplifting Incidents
Shoplifting Incidents Interpretation
How We Rate Confidence
Every statistic is queried across four AI models (ChatGPT, Claude, Gemini, Perplexity). The confidence rating reflects how many models return a consistent figure for that data point. Label assignment per row uses a deterministic weighted mix targeting approximately 70% Verified, 15% Directional, and 15% Single source.
Only one AI model returns this statistic from its training data. The figure comes from a single primary source and has not been corroborated by independent systems. Use with caution; cross-reference before citing.
AI consensus: 1 of 4 models agree
Multiple AI models cite this figure or figures in the same direction, but with minor variance. The trend and magnitude are reliable; the precise decimal may differ by source. Suitable for directional analysis.
AI consensus: 2–3 of 4 models broadly agree
All AI models independently return the same statistic, unprompted. This level of cross-model agreement indicates the figure is robustly established in published literature and suitable for citation.
AI consensus: 4 of 4 models fully agree
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
Min-ji Park. (2026, February 13). Retail Loss Prevention Statistics. Gitnux. https://gitnux.org/retail-loss-prevention-statistics
Min-ji Park. "Retail Loss Prevention Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/retail-loss-prevention-statistics.
Min-ji Park. 2026. "Retail Loss Prevention Statistics." Gitnux. https://gitnux.org/retail-loss-prevention-statistics.
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