Payroll fraud remains a persistent risk for employers—and analytics is increasingly central to the response. According to the Association of Certified Fraud Examiners (ACFE), payroll fraud is among the most common forms of occupational fraud, and 64% of organizations expand their use of data monitoring and analytics after experiencing an incident. That backdrop shaped discussions during one of the programs at the 2025 PayrollOrg Leaders Conference, where attendees examined how statistical techniques can enhance payroll oversight without disrupting operations.
In a session on foundational payroll analytics, Candace White, CPP, Director of Payroll Administration and Training, highlighted Benford’s Law as a practical early-warning tool for identifying potential fraud. Long used by forensic accountants and cited in IRS analytics, the technique helps payroll teams flag anomalies in transactional data for deeper review. White emphasized that Benford’s Law is a screening method—not a conclusion—designed to guide attention toward areas that may warrant further investigation.
What Benford’s Law Shows—and Why It Matters
Benford’s Law observes that in many naturally occurring datasets spanning several orders of magnitude, smaller leading digits appear more frequently. For example, the digit 1 appears as the first digit about 30% of the time, while 9 leads fewer than 5% of values. This logarithmic distribution often holds in transactional data not bounded by fixed price points or policies.
- Payroll relevance: Categories such as overtime, manual checks, adjustments, and reimbursements can meet Benford’s conditions, especially when values vary widely across employees, pay periods, and locations. If leading-digit frequencies in these categories diverge significantly from the expected pattern—or from an organization’s historical baselines—that discrepancy can justify targeted follow-up.
- IRS application: The IRS has publicly discussed using Benford-type tests in its risk models, including on Schedule C expenses for sole proprietors. Unusual clustering of high leading digits (such as 8s and 9s) may indicate fabricated or inflated amounts and can prompt additional scrutiny. White noted that payroll teams can apply similar logic to internal datasets—such as scanning overtime or adjustment amounts at the department level—then reviewing outliers in context, including policy changes, seasonality, or staffing shifts.
A Documented Pattern in Payroll Investigations
Forensic accounting literature, including Benford’s Law: Applications for Forensic Accounting, Auditing, and Fraud Detection by Mark J. Nigrini, documents cases where an overrepresentation of high leading digits in payment amounts signaled potential manipulation. In one example, repeated amounts such as 9,500 or 9,800 drove an abnormal share of 9-leading transactions, ultimately helping investigators focus on a small subset of suspicious payments and uncover a scheme involving fictitious payees.
The takeaway for payroll practitioners: Benford’s Law can compress a large review into a manageable set of transactions that merit closer inspection.
Limits—and How to Use It Responsibly
- Best-fit datasets: Benford’s Law performs best on large, heterogeneous datasets that are not artificially constrained. Small samples or narrow ranges—such as fixed stipends or standardized allowances—may be poor candidates and can produce misleading signals.
- Context is critical: Legitimate business events—mergers, wage-scale changes, retro pay, or bonus programs—can shift digit patterns. Analysts should corroborate anomalies with policy documentation, timekeeping logs, and approvals.
- Not a standalone test: Treat Benford’s results as triage. Pair them with trend analysis, peer-group comparisons, user access reviews, segregation-of-duties checks, and follow-up inquiries.
How Payroll Teams Can Put It to Work
- Start with the right categories: Overtime, supplemental pay, manual checks, off-cycle payments, adjustments, reimbursements, and vendor-like payees (e.g., garnishment remitters) often yield informative patterns.
- Establish baselines: Compare current-period leading-digit distributions to both Benford’s expected frequencies and your organization’s historical distributions by business unit and pay group.
- Investigate outliers methodically: For digits or categories that deviate materially, review source documentation, approval chains, user activity, and timing. Look for repeat amounts, round numbers near policy thresholds, and concentration by preparer or approver.
- Automate and monitor: Build recurring reports or dashboards that compute leading-digit frequencies and flag deviations beyond defined thresholds. Include explanatory notes so operational changes don’t trigger unnecessary alerts.
- Document decisions: Record why an anomaly was escalated or cleared to strengthen your control environment and support audit readiness.
The Bottom Line
As White reminded attendees, Benford’s Law is not a fraud detector—it’s a filter that helps professionals focus their attention. With the IRS and forensic practitioners using leading-digit analysis as part of broader analytics programs, payroll departments, professionals, and practitioners can adopt similar, measured approaches to improve detection, streamline audits, and reinforce controls.
In a landscape where payroll fraud remains a top operational risk—and most organizations expand analytics only after an incident—the 2025 PayrollOrg Leaders Conference underscored that responsibly applied simple statistical screens can meaningfully strengthen payroll integrity.