Key Takeaways
- According to the 2022 ACFE Report to the Nations, occupational fraud schemes were detected in 52% of surveyed organizations globally
- The PwC Global Economic Crime Survey 2022 found that 46% of businesses reported experiencing economic crime in the past 24 months
- KPMG's 2023 Fraud Barometer indicated fraud cases rose by 5% year-over-year in corporate sectors
- Global median loss per case of occupational fraud was $117,000 in 2022 per ACFE
- US companies lose $800 billion annually to fraud according to ACFE estimates
- PwC 2022 survey: average economic crime cost businesses $1.38 million per incident
- Asset misappropriation is the most common fraud type at 86% per ACFE 2022
- Corruption schemes occurred in 43% of ACFE 2022 cases
- Billing fraud tops schemes at 30% median duration 18 months per PwC 2022
- 42% frauds detected by tips per ACFE 2022 Report
- Internal audits detected 15% of cases per ACFE 2022
- Management review found 13% per ACFE data
- SEC investigations led to 460 actions in FY2022
- DOJ 2022: 45 corporate executives criminally charged for fraud
- Average prison sentence 27 months per ACFE 2022 convictions
Fraud is pervasive and costly for companies across industries worldwide.
Detection Methods
Detection Methods Interpretation
Financial Losses
Financial Losses Interpretation
Legal Consequences
Legal Consequences Interpretation
Prevalence
Prevalence Interpretation
Types of Fraud
Types of Fraud 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.
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.
Sophie Moreland. (2026, February 13). Corporate Fraud Statistics. Gitnux. https://gitnux.org/corporate-fraud-statistics
Sophie Moreland. "Corporate Fraud Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/corporate-fraud-statistics.
Sophie Moreland. 2026. "Corporate Fraud Statistics." Gitnux. https://gitnux.org/corporate-fraud-statistics.
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