AI Can Flag Wage Theft Risk Before EPLI Claims Hit

UPS is facing a New York lawsuit alleging about $45 million in unpaid wages over six years for seasonal workers—tens of thousands of people brought in from October to January to keep holiday deliveries moving. The allegations aren’t exotic: workers clocking in after their shift started, and automatic lunch deductions for breaks they didn’t take.

From an EPLI (Employment Practices Liability Insurance) perspective, that’s the point. Wage-and-hour exposure rarely starts as a dramatic scandal. It starts as “how we’ve always done timekeeping,” multiplied by temporary staff, peak volume, and managers trying to keep routes on schedule.

This post is part of our AI in Human Resources & Workforce Management series, and I’m going to take a clear stance: most wage-theft losses are detectable weeks or months earlier—not because AI is magic, but because wage-and-hour problems leave patterns. If you’re an insurer, a broker, a risk manager, or an HR/ops leader, those patterns can be scored, monitored, and acted on before they become lawsuits, class actions, and seven-figure claim reserves.

What the UPS lawsuit signals for EPLI and wage-and-hour risk

The clearest lesson is simple: seasonal labor amplifies compliance mistakes into balance-sheet events. When you hire thousands of temporary workers quickly, the organization relies on standardized processes—time clocks, payroll rules, supervisor approvals—and small defects scale.

In the UPS case, New York’s attorney general alleges practices that are common across many large employers during peak season:

• Off-the-clock work: workers starting tasks before being allowed to clock in, or clocking in “late” due to workflow bottlenecks.
• Meal-break auto-deductions: pay reduced for a lunch period that wasn’t taken.
• Role complexity: “driver helpers” and “seasonal support drivers” have different workflows and supervision models, which increases inconsistent time capture.

For insurers underwriting EPLI or related wage-and-hour endorsements, this matters because the cost stack isn’t just back pay. It can include penalties, attorneys’ fees, defense costs, operational remediation, reputational risk, and follow-on claims when an investigation opens the door to other issues.

For employers, the lesson is uncomfortable: “We didn’t intend to underpay” doesn’t stop damages from accruing. Intent is a legal question; exposure is a math problem.

Why holiday peak season is a perfect storm

December is when timekeeping controls tend to fail:

• Supervisors are overloaded and approve timecards in batches.
• Schedules are fluid, start times shift, and workers show up early to get assignments.
• Temporary staff don’t know escalation paths.
• Mixed fleets and bring-your-own-vehicle delivery models add complexity.

If you’re thinking, “That sounds like a workforce management problem,” you’re right. And that’s why AI in HR and workforce management is becoming a risk-control function—not just an efficiency play.

The hidden data trail: how wage theft shows up in patterns

Wage theft (or wage-and-hour noncompliance) creates detectable anomalies. You don’t need perfect data; you need enough signals.

Here are patterns that consistently correlate with wage-and-hour disputes and EPLI claim frequency:

1) Repeated “late clock-ins” clustered by site or supervisor

If one location has an unusually high rate of late punches compared to similar sites, the question isn’t “Are workers lazy?” The better question is:

Is the clock-in process forcing people to work before they can punch?

AI models can baseline expected clock-in distributions by role and site, then flag deviations that persist. Even basic anomaly detection will catch clusters.

2) Meal-break deductions that don’t match actual break behavior

Auto-deduct policies aren’t inherently illegal; the risk is when they’re not paired with frictionless attestation and exception handling.

Signals include:

• The same 30-minute deduction applied to nearly every shift (even very short shifts).
• High exception rates but low correction rates.
• Break timestamps missing in systems that should capture them (mobile apps, handheld scanners, telematics stops).

AI doesn’t have to “prove” a missed lunch. It can do something more useful: identify where your process makes missed lunches likely.

3) Timecard edits that trend one way

If edits disproportionately reduce paid time (rather than correcting both up and down), that’s a risk flag.

A practical metric I like is an “edit directionality index” by manager:

• % of edits that decrease payable minutes
• average minutes removed per edit
• edit frequency per 100 shifts

When those spike for a specific supervisor or site, you have a controllable risk.

4) BYOV (bring-your-own-vehicle) delivery and “gray time”

Seasonal support drivers using personal vehicles can create gaps between:

• assigned route start
• app login
• first scan
• first delivery timestamp

Those gaps are where gray time lives (loading, staging, instructions, waiting). AI can reconcile timestamps across systems to highlight roles where paid time consistently undercounts operational time.

Where AI fits: prevention beats prediction

AI in workforce analytics isn’t just about forecasting staffing levels. Used correctly, it becomes a compliance early-warning system.

Here’s the best framing:

Rules catch known violations. AI catches patterns of process failure.

AI techniques that work well for wage-and-hour monitoring

• Anomaly detection: flags sites, supervisors, and roles whose timekeeping patterns drift from normal.
• Natural language processing (NLP): scans HR tickets, hotline complaints, exit interviews, and chat logs for wage-related phrases (e.g., “worked through lunch,” “told to clock in later,” “unpaid training”).
• Entity resolution: connects multiple worker records (temp agency IDs, payroll IDs, badge IDs) so issues don’t hide in system fragmentation.
• Causal diagnostics (practical version): compares similar sites (same role mix, volume, geography) to identify what’s different operationally when wage anomalies rise.

This isn’t theoretical. Most large employers already have the raw ingredients: time clocks, payroll, scheduling tools, route/scan events, and HR case systems. The missing piece is cross-system analysis and governance.

What insurers can do: AI-driven underwriting and claims triage for EPLI

Insurers don’t need to become HR departments, but they can get smarter about wage-and-hour exposure using AI—especially when the market is under pressure to price risk more accurately.

Underwriting: score process maturity, not just headcount

Traditional inputs (employee count, industry class, prior claims) are necessary but incomplete. The UPS allegations highlight why process maturity matters.

An AI-assisted underwriting approach can incorporate:

• Timekeeping control indicators: auto-deduct policies, exception workflow SLAs, audit cadence.
• Workforce volatility: seasonal hiring spikes, temp-to-perm ratios, multi-site variance.
• Operational time signals: roles with high pre-shift coordination or travel time.

This doesn’t require invasive employee surveillance. It can be built from aggregated metrics and employer attestations—then validated during audits.

Claims: detect wage-theft “signature” early

Wage-and-hour matters can escalate quickly. AI can help claims teams:

• classify incoming allegations (off-the-clock, meal breaks, rounding, training time)
• estimate likely scope (single site vs. systemic)
• prioritize document requests (time edits, exception logs, policy acknowledgments)

That speeds up reserving and improves early settlement decisions when liability is clear.

Risk engineering: give policyholders something they’ll actually use

Policyholder-facing tools should be concrete. A dashboard that says “High risk” isn’t helpful. A dashboard that says:

• “Site A has 3.2× the late-punch rate of comparable sites”
• “Manager B edits timecards 4× more than peers and 91% of edits reduce payable minutes”

…creates a clean path to corrective action.

What HR and operations leaders should implement now (before next peak season)

If you’re running seasonal labor—or any distributed workforce—these are the controls I’d put in place ahead of the next surge. They’re also the controls insurers increasingly expect to see.

1) Replace silent auto-deductions with worker attestation

If you keep auto-deduct, pair it with a simple, auditable flow:

• worker confirms break taken (one tap)
• worker can report “no break” without retaliation risk
• exceptions route to same-day review, not end-of-pay-period cleanup

2) Instrument the “start of shift” workflow

Most off-the-clock problems happen before the first punch: getting assignments, loading, waiting, mandatory huddles.

Fix the workflow so:

• clock-in is available before required pre-shift tasks
• supervisors aren’t gatekeepers to the punch
• system logs show when workers were instructed to begin duties

3) Monitor timecard edits like financial transactions

Treat edits as controlled events:

• require reason codes
• log editor identity and timestamps
• audit managers with unusual patterns

A good internal standard: timecard edits should be rare, symmetric, and explainable.

4) Build a “wage-and-hour anomaly review” cadence

Weekly beats monthly. During peak season, daily is better.

Minimum viable meeting agenda (30 minutes):

1. sites with highest late-punch growth
2. break exception volumes and resolution times
3. outlier managers for edits
4. worker complaints tagged to pay/timekeeping

5) Put guardrails around AI so it doesn’t create new risk

AI can backfire if it’s used to police individuals unfairly. Keep it focused on process health:

• use aggregated monitoring first (site/role/supervisor level)
• document model purpose: compliance and process improvement
• run bias checks on alerts (are certain groups disproportionately flagged?)
• keep humans in the loop for decisions impacting pay or discipline

People also ask: practical questions about AI and wage-theft compliance

Can AI prove wage theft?

AI usually won’t “prove” it on its own. What it does well is surface patterns that warrant investigation and help teams prioritize audits before exposure balloons.

What data do we need to start?

Start with what you already have: payroll, timekeeping, schedules, timecard edits, and HR case notes. The fastest wins come from joining timekeeping + payroll + edits.

Does this create privacy or employee relations issues?

It can—if you frame it as surveillance. Frame it as pay accuracy and fairness, focus on process-level signals, and be transparent about how alerts are used.

The bigger point for AI in workforce management

This UPS lawsuit is a reminder that pay compliance is a trust system. When it breaks, it doesn’t just trigger a legal case; it damages retention, hiring, and brand credibility—especially in December, when seasonal workers talk.

For insurers, AI can strengthen underwriting and claims handling by turning wage-and-hour risk into something measurable. For employers, AI workforce analytics can serve a more human purpose: making sure people get paid correctly, every time, even during peak chaos.

If you’re heading into 2026 planning, here’s a useful gut check: If a regulator asked you tomorrow to explain your lunch deduction accuracy and off-the-clock controls—could you answer with data, not anecdotes?