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Construction & Infrastructure

Timesheet Fraud Detection for a Metro Construction Consortium

Anonymised engagement
Client
Construction consortium · Europe · metro line build, 1,200+ people through its site gates
Industry
Construction & Infrastructure
Department
Risk, Compliance & Audit
Region
Europe

Every clock-in stamped for an absent colleague is an hour paid for work nobody did, and with 61,000 time records a year nobody can spot it by eye. We built a UiPath robot that found the clock-ins too perfectly in sync to be honest and checked each one against 1.4 million site gate events. About 14% of the staff reviewed showed malicious clocking behaviour.

A working model of the approach on sample data. It plays on its own and pauses when scrolled away; nothing here touches a live system.

The results

14%
of reviewed staff flagged for clocking fraud
Audit finding reported by the delivery team, out of 154 employees reviewed.
61,000
daily time records analysed
61,138 records for 154 employees over 13 months, parsed from the timekeeping report.
1.4M
gate events cross-checked
About 1.39 million access-control events across 98 site locations.

The challenge

Our client is a construction consortium building a metro line through the centre of a major European city: dozens of live sites, underground stations and shafts, and more than 1,200 people badging through the gates in a year. Staff clocked in and out on a timekeeping system, and everyone entering a site also passed an access-control gate. The two systems had never been compared.

That left room for two kinds of abuse: an employee clocking in or out for a colleague who was not there, known as buddy punching, and employees stamping more hours than they worked. The company commissioned an internal audit to find out how much of it was happening.

What it was costing them

  • Paying for hours nobody worked. Every fraudulent stamp is paid as a worked hour, plus employer contributions.
  • Invisible at scale. 61,000 daily time records and 1.4 million gate events a year cannot be compared by hand.
  • Safety and site security. A person recorded as on site who is not, or the reverse, is a real problem on a live underground construction site.
  • Contagion. Abuse that is never detected spreads, because colleagues see it go unchallenged.
  • A weaker position on costs. On a project where labour cost is passed through and contested, inflated hours undermine every figure the company has to defend.
How a buddy punch shows up
  • Two clock-ins in one minute
  • Compare with gate events
  • One person on site
  • One clock-in with no gate record

The approach

We built a UiPath robot as the analytics engine of the internal audit. It turned the company's own timekeeping reports into data, looked for clock-ins that were statistically too synchronised to be genuine, and tested every suspect against the access-control system.

From timekeeping report to audit evidence

  1. Report to data. The robot read the timekeeping report (PDF), parsed every employee block and day with pattern matching, and built a table of 61,138 daily records for 154 employees over 13 months: role, site, up to three entries and exits a day, and totals.
  2. Synchronised clock-in detection. For every day it compared each employee's entry and exit times with every other employee's, and flagged pairs who clocked in and out within 0, 1, 2, 3 or 4 minutes of each other: the signature of one person stamping two badges.
  3. Weekend-work check. Saturday and Sunday stamps counted per employee as a second indicator.
  4. Cross-check against the gates. Every suspect's recorded shifts were checked, month by month, against roughly 1.4 million access-control events across 98 site locations, to confirm whether the person actually passed the gate.
  5. Evidence for the auditors. Results written to workbooks per check and per time interval, with the matching gate events alongside, so the audit team could review every case.
From time records to flagged staff
  • 61,138 daily time records
  • < 30 matching pairs per interval
  • 14% of 154 staff flagged
Every suspect was checked against about 1.4 million gate events.

From thousands of coincidences to a reviewable list

Same-minute entries alone are common on a busy site: more than 2,000 entry-only coincidences at zero minutes apart. Requiring both entry and exit to match cut that to fewer than 30 pairs per interval, and the gate cross-check then put each suspect's own badge events beside the clock record. The auditors reviewed evidence, not a statistic.

Built for the investigation

Fast, single-purpose analytics run under audit direction, on records the company already held, rather than a permanent production robot.

Technology

LayerWhat we used
Automation and analyticsUiPath Studio, data-table and Excel activities, VB.NET
Document parsingPDF text extraction, regular expressions
SourcesTimekeeping report (PDF), access-control export (Excel, monthly)
OutputAudit evidence workbooks per check and interval

The outcome

The audit team got what it could never have produced by hand: every suspect clock-in, side by side with the gate record that confirms or contradicts it.

BeforeAfter
Timekeeping vs site accessNever comparedReconciled for 13 months
Scope of the checkNot feasible by hand61,138 time records and ~1.4M gate events
Buddy punchingInvisible record by recordFlagged at 0-4 minute intervals
EvidenceNo systematic checkA workbook per check, with the matching gate events
  • About 14% of the employees reviewed showed malicious clocking behaviour.
  • Several hundred hours paid but not worked uncovered over the period reviewed. At an average construction labour cost of about €24 an hour, that is roughly €7,000–€22,000 (our estimate).
  • 13 months of timekeeping and 1.4 million gate events reconciled, an exercise that is not feasible by hand.
  • Evidence per case for the internal audit investigation.

The risk most teams miss

A timekeeping system checks that a badge was used, not who used it. Fraud that relies on a colleague's hand looks perfectly normal record by record; it only shows when records are compared with each other and with a second, independent system. The other trap is employee-monitoring law: checks on staff are regulated, GDPR included, so the analysis has to run on records the company already lawfully holds, under an audit mandate, with findings reviewed by people before anything is acted on.

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