Your Attendance Data Is Being Manipulated. We'll Prove It.
Most HR teams suspect time theft but can never prove it. MAttendance runs 8 AI-powered anomaly analyzers every night — flagging buddy punching, ghost employees, location fraud, and abnormal patterns with timestamps, device IDs, and GPS evidence.
The Problem With Manual Oversight
Traditional HR tools generate attendance data. They don't help you understand it.
You Suspect It. You Can't Prove It.
HR managers know when something feels off — an employee always present but never productive, punch records that don't match reality. Without hard evidence, nothing can be done.
Manual Audits Are Unsustainable.
Reviewing hundreds of punch records by hand to find irregularities is a full-time job in itself. One person cannot audit 500 employees daily — the math doesn't work.
Fraud Compounds Over Time.
Every day a buddy-punching pair goes undetected is another day of payroll fraud. At scale, even 5 minutes of extra paid time per employee per day becomes a significant monthly cost.
MAttendance runs 8 specialized AI analyzers every night — automatically reviewing every punch record so you don't have to.
Every Analyzer. What It Catches.
Each analyzer targets a specific fraud pattern with its own detection logic and evidence trail.
Buddy Punching Detector
Employees who punch in on behalf of absent colleagues — the most common form of attendance fraud.
How It Works
Looks for patterns where multiple employees consistently punch in at nearly identical times, from the same device, or where Employee A's punch correlates suspiciously with Employee B's pattern when B is absent.
Evidence Provided
- Employee pair(s) involved
- Dates and timestamps of suspected incidents
- Device ID match (if applicable)
- Probability score
Why it matters: In a 500-person organization, even 10 buddy-punching pairs = 20 fake attendance records per day.
Rapid Punch Pattern Detector
Suspiciously fast in-out-in sequences — a common pattern when employees mark attendance and immediately leave.
How It Works
Flags any employee who punches in, then out, then in again within an abnormally short window (configurable threshold). This indicates "ghost punching" — recording presence without staying.
Evidence Provided
- Employee name
- Punch sequence with timestamps
- Time delta between punches
- Frequency of this pattern
Unusual Hours Detector
Punches at abnormal times — 3 AM clock-ins, midnight clock-outs, or patterns outside shift windows.
How It Works
Compares punch timestamps against the employee's assigned shift schedule. Flags any punch occurring outside a reasonable window relative to their shift.
Evidence Provided
- Employee name and shift
- Punch time vs. expected shift window
- Deviation in minutes/hours
- Historical frequency
Why it matters: Unusual hour patterns often indicate system manipulation or punches being entered retroactively.
High-Frequency Break Analyzer
Employees taking excessive or suspiciously frequent breaks, inflating paid time.
How It Works
Analyzes break start/end records and flags employees whose break count, duration, or frequency is statistically abnormal compared to their peer group and shift policy.
Evidence Provided
- Break log for flagged day
- Total break time vs. policy allowance
- Comparison to department average
Exact Time Pattern Detector
Employees who punch in at exactly the same time every day — to the second. This robotic precision signals manual data entry.
How It Works
Analyzes the standard deviation of punch times over a rolling window. Legitimate employees show natural variation. Manually entered records show near-zero variation.
Evidence Provided
- Punch times over last 30 days
- Standard deviation score
- Pattern visualization
- Confidence level
Why it matters: If an employee punches at exactly 09:00:00 every day for 30 days, that's a manipulation signal, not a coincidence.
Location Mismatch Analyzer
GPS punch coordinates that don't match the employee's assigned office geofence.
How It Works
For GPS-based punches, compares the submitted coordinates against the employee's assigned office geofence. Flags punches where coordinates are outside the allowed radius.
Evidence Provided
- Submitted GPS coordinates (lat/long)
- Assigned office location
- Distance from office boundary
- Map view of punch location
Why it matters: Catches employees who are not physically at the office despite submitting GPS punches — including GPS spoofing.
Device Change Detector
Employees who suddenly start punching from unfamiliar or frequently rotating devices — a signal someone else is submitting their punch.
How It Works
Builds a device profile for each employee. Flags any punch from a device outside their established profile, especially new devices not associated with any other employee.
Evidence Provided
- Normal device(s) for this employee
- Flagged device ID and first-seen date
- Other employees who have used this device
IP Mismatch Analyzer
Web browser punches submitted from IP addresses outside the expected office network range.
How It Works
For Web punches with IP restrictions configured, cross-checks punch IPs against registered office IP ranges. Flags any out-of-range punch not explicitly approved.
Evidence Provided
- Submitted IP address
- Expected IP range for the office
- Geolocation of the IP (city/region)
- Time of punch
Predictive Analytics That Get Smarter Over Time
Beyond anomaly detection, MAttendance uses a Microsoft ML.NET FastTree model trained on your organization's own historical data to forecast attendance problems before they happen.
The model retrains automatically every week, incorporating your latest attendance patterns. The longer you use MAttendance, the more accurate your predictions become.
Absenteeism Risk Score
Each employee receives a weekly risk score based on historical absence patterns, seasonal trends, and peer group behavior. HR can proactively reach out before patterns become problems.
Chronic Lateness Trends
Identify departments or teams trending toward systemic lateness — before it becomes a culture problem.
Shift Coverage Forecasting
Predict which shifts are likely to have attendance gaps in the coming week, enabling proactive scheduling.
Overtime Cost Projections
Based on current attendance trends, project overtime payroll costs for the month before payroll runs.
What Happens While You Sleep
Every night, MAttendance runs its full analysis pipeline automatically — no cron jobs to configure, no manual triggers needed.
11:00 PM
System captures final punches of the day
12:00 AM
Holiday & week-off marking job runs
01:00 AM
All 8 anomaly analyzers run in parallel
01:30 AM
Anomaly severity scoring and deduplication
02:00 AM
Dashboard updated with new anomaly flags
07:00 AM
HR Manager receives daily anomaly digest email
Anomaly Severity Levels
Every flagged anomaly is scored and categorized so HR knows exactly where to focus first.
High-confidence fraud signal — immediate review needed
Strong pattern — likely requires investigation
Noteworthy pattern — monitor closely
Informational flag — minor deviation
Every flag includes evidence. MAttendance never asks HR to act on a hunch — every anomaly comes with timestamps, device IDs, GPS coordinates, and a confidence score.
AI Detection Questions
Answers before you commit to a system.
Buddy punching is when one employee clocks in or out on behalf of another absent employee. It's the most common form of attendance fraud, where Employee A punches in for Employee B who hasn't arrived yet or has already left.
MAttendance's Buddy Punching Analyzer looks for statistical correlations between employees' punch records — same device, same time patterns, or coordinated absences. It builds a probability score and flags high-confidence pairs for HR review.
The analyzers are designed around patterns, not individual incidents. An employee might avoid detection for one day, but consistent patterns over weeks are statistically impossible to hide. The exact-time pattern detector specifically catches manual data entry even when the manipulator thinks they're being careful.
Yes. MAttendance uses Microsoft ML.NET with a FastTree model, trained weekly on your organization's historical attendance data. The model improves over time as it learns your specific workforce patterns.
No. Anomaly detection results are visible only to HR Managers, Admins, and authorized reviewers. Employees are not notified of flags unless the organization chooses to take formal action.
False positive rates vary by analyzer. The exact-time pattern and buddy punching detectors use confidence thresholds to minimize false positives. Every flag includes the underlying evidence so HR can make an informed judgment rather than acting on an alert alone.
Threshold customization is available for enterprise plans. The system ships with research-calibrated defaults that work well for most organizations.
See What's Hidden In Your Attendance Data.
Book a demo and we'll walk you through a live anomaly report — using realistic examples from the types of fraud your industry sees most.
No commitment. Most teams find their first anomaly within the first week.