5 Ways Workers Cheat Biometric Attendance (And How to Stop It)

Labour cost is 30 to 40 percent of manufacturing costs. A 10 percent attendance fraud rate equals 3 to 4 percent total cost increase, hitting your bottom line directly.

But the money is only half the damage. Production planning fails when you plan for 100 workers and only 85 actually work. Quality suffers because tired workers doing fake overtime cut corners. Honest employees get demoralised when they see fraud rewarded. EOBI contributions are inflated by ghost attendance and labour department inspections expose the gaps.

Employee A marks attendance for absent Employee B. The most common fraud in Pakistani factories. “Yaar log in kar do, late ho gaya.”

Older biometric devices have weak fingerprint quality checks. They can be fooled with photos of fingerprints, tape and glue impressions or gel-based fake prints. Or just simple collusion: Employee A arrives at 8 AM, marks for B who shows up at 10. The supervisor has not arrived yet, nobody notices. The device captures a fingerprint but no face, so there is no proof of who actually punched.

Employees mark entry and exit to create false overtime hours without actually working those hours.

The mechanics are straightforward. Mark in at 6 AM (official start 8 AM) and claim 2 hours of OT. Actually arrive at 7:45 AM, go for chai, come back at start time. Or mark out at 8 PM (official end 5 PM) for 3 hours of OT but actually leave at 5:15 PM after spending the rest in the canteen, sleeping in the car or going home. The “perfect crime” is to mark in early, leave the premises, return for the actual shift, mark out late and immediately leave, all while claiming 4 to 5 hours of daily OT.

It works because OT approval is based on attendance data alone, not actual output. Production supervisors are not in the OT approval loop. The system never flags unusual patterns like the same worker always marking in 2 hours early.

Supervisor and workers reach an informal arrangement. Workers take unauthorised breaks, supervisor adjusts records or allows buddy punching, money or favours change hands.

A typical deal: workers pay the supervisor PKR 500 to 1,000 weekly. In exchange the supervisor allows late arrivals, early exits and buddy punching, or the workers do the supervisor’s personal errands. Group collusion takes it further. The whole shift covers for each other, the supervisor marks everyone present, workers rotate days off, and production still gets met because the workers actually present compensate.

It is hard to detect because the biometric data looks normal, production targets get hit, and only detailed pattern analysis reveals the rotation. The Friday afternoon scenario is classic: 5 workers leave at 2 PM for Jummah, do not return, supervisor marks exit at 5 PM, workers each pay PKR 500. Supervisor pockets PKR 2,500 a Friday, workers get 3 paid hours off.

Attendance records get altered after the fact. Unauthorised absences become authorised leave. Records are tampered.

If the system allows manual editing, a clerk who is the worker’s relative converts 5 unauthorised absences to “sick leave with pay” with no approval, no verification. Half-day manipulation is similar: came late, worked half a day, marked as full-day present, manager misses it in monthly data. Leave balance fraud goes deeper, the system shows 2 leaves remaining, the worker actually took 8 this year, somebody with backend access adjusted the balance.

Add it up for a typical 100-employee Pakistani steel pipe factory and the numbers stop being abstract.

PKR 6.9 lakh a month. PKR 82 lakh a year. And that is before the hidden costs: best workers leaving, planning errors, quality defects from fake-OT fatigue, and the competitive disadvantage of running with bloated payroll while your tighter-controlled competitor wins on price.

Your PKR 3 lakh biometric investment is not stopping fraud because the device alone solves only one problem (identity verification at the moment of punch) and leaves the other four problems wide open.

The data sits inside the device. Somebody manually exports it to Excel. Excel is trivially manipulable. There is no real-time monitoring, no production cross-check, no supervisor verification, no pattern detection, no anomaly alert and no audit trail. The device just records attendance, it does not analyse it.

Pipetal’s HR & Payroll module plugs directly into ZKTeco fingerprint and face devices, and cloud-based ZKBioTime. The device is no longer the system, it is just a sensor feeding the system.

Photo verification at the moment of punch eliminates buddy punching because face plus fingerprint must match. OT approvals require a production supervisor sign-off tied to actual work orders, killing fake-OT inflation. Multi-level approval workflows make supervisor-worker collusion visible from the management dashboard. Manual edits are not deletes, they are logged with who, what, when and why, so the audit trail is tamper-proof. Real-time alerts flag unusual patterns the moment they appear, not 30 days later in payroll.

A 120-employee Faisalabad steel factory running 3 shifts had standalone biometric plus Excel and a PKR 48 lakh monthly payroll. They suspected fraud but could not prove it.

In the first month after Pipetal HR went live, the system surfaced 18 instances of buddy punching (caught by photo verification), 12 employees routinely claiming 2+ hours of false OT, and supervisor-worker collusion in the night shift. The owner used a 30-day grace period with warnings and clear rules instead of immediate action.

After six months, monthly wage leakage dropped by PKR 3.2 lakh, on-time attendance rose from 72 to 91 percent, OT hours fell 35 percent without any drop in production, and honest employees were visibly happier because the cheating they always knew about had finally stopped.

The wrong way is to ambush the staff. Workers will revolt, supervisors will sabotage and honest employees will get caught in the crossfire. The right way is to announce transparently, train everyone, start with warnings, then enforce strictly.

Step 1, announce that the new system is here to protect honest workers. Step 2, train every shift on what is acceptable, what is not, and what the consequences are. Step 3, give a 30-day grace period where the system flags but does not penalise. Step 4, after the grace period, enforce consistently with no exceptions. Step 5, reward perfect attendance publicly so the culture starts pulling honest workers up rather than dragging everyone down.