Must payroll errors be corrected immediately, or can the correction wait until the next payroll cycle?
The answer likely depends on the state in which the employee works and receives pay. Under both state and federal regulations, employers are required to pay employees for all wages earned and due, in accordance with the designated pay period. In many circumstances, state regulations provide more detailed requirements regarding the timing of wage payments. For example, California requires that wages earned between the 1st and the 15th of the month be paid by the 26th of the month. Other states require wages to be paid within a certain number of days following the end of the pay period in which they were earned.
Despite the best efforts of employers, there are times when hours worked are not included in the employee's pay for one of a number of possible reasons. The likelihood of corrections increases if the organization has established a payroll cycle of "paying current." This payroll cycle involves projecting hours worked for the end of the pay period to process payroll earlier in the pay week.
Some states have specific guidelines for correcting an underpayment mistake. For instance, Oregon allows employers to wait until the following pay period if the amount of underpayment is less than 5 percent of the total paycheck. Florida, for example, does not have a state regulation governing the timing of wage payments or required corrections.
Absent specific state requirements, it is important for an employer to ensure that employees are paid in a timely manner for all wages earned. It is recommended that employers make payroll corrections immediately and not wait until the next pay period. Employers should also investigate the cause of the payroll error and make the necessary corrections to avoid future wage payment errors.
An organization run by AI is not a futuristic concept. Such technology is already a part of many workplaces and will continue to shape the labor market and HR. Here's how employers and employees can successfully manage generative AI and other AI-powered systems.