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highlighting the importance of data preparation, validation, and integration maturity. The broader takeaway is more important than the individual study: many payroll failures begin before the actual information enters payroll, even the most accurate local payroll engine can produce the wrong outcome. If employee information is transformed incorrectly between systems, the calculation may technically be correct based on the data received while still producing an incorrect result for the employee. If the payroll output then moves through additional systems for funding, payments, tax, accounting, and reconciliation, further failure points are introduced. This raises an important question for global payroll leaders: how many systems should employee data have to pass through before someone gets paid? Every additional handoff creates another opportunity for information to be changed, delayed, rejected, payroll calculation starts. If incorrect or incomplete duplicated, incorrectly mapped, or lost. It also creates another location where accountability can become unclear. When payroll is wrong, teams may need to determine whether the
capable of identifying payroll problems before they reach an employee. Those expectations are fundamentally different from the problems the traditional aggregator model was originally built to solve. The nature of payroll risk is also changing. Payroll accuracy is no longer determined solely by whether a local calculation engine correctly applies tax and statutory rules. Increasingly, payroll performance depends on the quality, consistency, and connectivity of the information entering the calculation. Recent industry research reinforces this point. CloudPay’s 2026 Global Payroll Efficiency Index, based on more than 4.7 million payslips across more than 140 countries, found that 57% of payroll payment issues originated upstream,
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GLOBAL PAYROLL MAGAZINE ISSUE 28
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