Time will tell: How AI detects attendance irregularities for payroll in real time

March 10, 2026

Drew Maginn

Whether punching a clock or logging onto a computer, employee monitoring and surveillance using artificial intelligence (AI) timekeeping software is becoming more common. While many employers are using AI timekeeping tools to improve payroll accuracy and reduce time theft, it’s important to understand how employers can integrate these tools responsibly without risking the privacy and trust of their employees.

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What is AI attendance tracking and how does it work?

For many payroll teams, AI attendance tracking is an attractive option for tracking each employee’s work patterns to ensure they are accurately paid and to identify any discrepancies that emerge outside their typical routine. These tools gather valuable data on each employee using a variety of systems that fit the nature of the job being performed. These may include:

Badge swiping (e.g., tapping an ID card);
Online logins (e.g., logging into a company app or system);

GPS tracking (e.g., using location data to track travel for jobs like delivery drivers);

Biometrics (e.g., using facial recognition or fingerprint scans); and

Communications (e.g., monitoring communications sent using work tools like email and chat).

While the method of collecting employee information varies, this data can be rolled up into summary reports that link directly to payroll processing needs, including hours worked compared to schedule, lates, absences and overtime.

” While many employers are using AI timekeeping tools to improve payroll accuracy and reduce time theft, it’s important to understand how employers can integrate these tools responsibly without risking the privacy and trust of their employees. ”

What can it tell us? Understanding the value of real-time attendance monitoring and payroll automation

There are several uses for attendance monitoring in payroll, but it’s typically easiest to understand them in relation to different types of workers.

For shift workers

Checking clock-in and clock-out times: Employee clock-in and clock-out data is monitored on an ongoing basis, and the system will detect any patterns that are out of the ordinary. For example, AI might flag an employee who typically clocks in five minutes before their shift but is now consistently clocking in 15 minutes late, which may require a conversation with their manager.
Flagging “buddy” punching: AI tools can identify odd behaviours to determine if an employee is misrepresenting their actual time worked. For example, two employees who consistently clock in or out each day within seconds of each other. This type of time fraud, called “buddy” punching, occurs when an employee checks themselves in along with an absent or late co-worker.
Monitoring unusual shift lengths: If employees are working unusual shift lengths, it will be flagged for further review. For example, an employee may work an unapproved double shift or simply forget to clock out at the end of the day.

For remote/hybrid workers

Monitoring flexible/remote work hours: AI can identify if employees are not logging in during their approved work hours or are “inactive” on the system for long periods of time. For example, if an employee regularly logs in on time each day but has little to no activity on the system, this may indicate they are not actively working their full hours.
Flagging unusual login locations: When an employee logs in remotely, their work location can also be identified. For example, an employee may be “working” from an unapproved location rather than taking the time as vacation as outlined in the organization’s policies and procedures.

For managers

Sending manager alerts: For many of the examples described above, AI tools can send automatic alerts to reporting managers so they are aware of any unusual patterns among their direct reports (e.g., employees logging in late, employees working unapproved overtime). This allows managers to be aware of these potential issues in real time so they can address them immediately, rather than finding out after weeks, or even months, of employee misconduct.

Detecting overwork or burnout: AI tools can also identify where employees may be active for long hours, missing breaks or consistently working on evenings or weekends. A manager should be aware of and avoid potential employee burn out, which could impact their job satisfaction, productivity and mental health and well-being.

Ethical automation: Maintaining employee trust while using an AI timekeeping compliance tool

When considering the use of any type of AI for employee monitoring and surveillance, a good rule of thumb is to proceed with caution. Employers must be transparent and accountable to their employees about the use to these tools to maintain their trust and support. For example, Canadian HR Reporter recently reported that AT&T faced severe backlash from employees for implementing an attendance tracking system that was intended to monitor in-office days but ended up being inaccurate and intrusive to the point that employees felt their privacy had been violated. While these systems can be used to effectively contribute to the accuracy of your payroll processes, issues related to data protection (e.g., employee location, facial recognition), access and storage need to be documented, communicated and always followed.

The accuracy and reliability of payroll is only as good as the data it uses. By utilizing AI tools and software to support attendance tracking and timekeeping, many employers feel more confident knowing that their processes are free from human error, whether intentional or not. However, as you consider employing AI to monitor your employees’ work habits, always take the time needed to weigh the good with the bad. While the gains in efficiency and tracking may be significant, they may not be worth it if they come at the expense of the trust and security of your workforce.

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