Payzaar CEO on Lohn + Gehalt: Process Automation in Global Payroll

Anyone who has ever worked in payroll knows that billing operations are often very manual and labor intensive. In addition to collecting and preparing data for payroll, one of the most time-consuming activities in many companies is reviewing the calculated payroll data that the external payroll service provider or the internal payroll team created. We recently spoke to the payroll department of a German car manufacturer and learned that it spends about 60 percent of its time reviewing payroll data for each employee before releasing the data.

In the case of this company, this literally means that members of the billing team spend countless man-days every month making sure the billing data processed by their local billing partners is accurate.

One way to reduce the time spent reviewing billing is to reconcile the current billing with earlier batches using a deviation analysis. Of course, the calculation values ​​can and will change from one payment period to the next (eg due to different working hours, sick days, overtime, bonuses, commissions, tax rate changes, etc.).

But by systematically reconciling individual employee values ​​across different payment periods, potential errors and outliers can be quickly identified. This is especially the case if the system is smart enough to take into account certain seasonal factors such as year-end bonuses, which are usually paid out at the same time of the year. For example, if the basic salary or health insurance deductions suddenly jump from one salary period to another, this event should be examined more closely by an employee of the payroll team.

While deviation analysis can lead to false alarms (indicating a bias that is perfectly legitimate because the circumstances for the employee have changed), it does help to focus on actual, significant deviations when reviewing the data by the billing teams steer and quickly clears the scan of all data points that are in the expected “normal” range. This allows the billing team to reduce the search of huge heaps of data to the “outlandish” outliers. For example, this kind of analysis of variance quickly highlights all employee entrances and exits over a given time period, as their values ​​increase or decrease by 100 percent.

The next step in refining this automated data validation method is to also consider the data entries themselves to judge whether the issues appear correct. For example, in this type of validation, the system detects that the number of shift hours submitted to the system for an employee has risen by 20 percent from one month to the next, and would check if the shift hour payouts show a similar increase. Similarly, the system can verify that implicitly calculated tax rates and social security rates are within the expected range (that is, they have not jumped irrationally from one period to the next).

In many ways, this is an excellent example of Artificial Intelligence (AI) that takes on the tedious and time-consuming tasks of humans. While a human needs hours and days to compare a large volume of data points and yet overlook certain deviations or patterns, the software can process huge amounts of data and perform smart reconciliations in seconds.

While this type of smart technology is becoming more commonplace, many smaller local payroll service providers do not have the resources to implement this type of automation and it is unaffordable for customers to develop their own automation solutions. At the same time, it is precisely the data from the “long tail” of the many small, often remote, country units that are particularly difficult for global payroll accountants to verify. In particular, if you need to look at data in foreign language and currency, it can be very difficult to understand exactly which billing data you are approving. A tool such as an automatic deviation analysis that allows you to quickly and comprehensively check for deviations makes it possible too.

The exciting thing about this kind of process automation is that it benefits both the customer and the local payroll clerks. It reduces the manual effort and thus increases efficiency both for the local contract processor and for the customer. More importantly, it reduces the number of mistakes made by employees’ payroll, reducing the amount of work required to investigate and correct errors that have occurred.

Would not it be fantastic if companies in this way could have a consistent, efficient way to reconcile and validate all their global payroll data for all employees, regardless of where they are, in large or small countries? And how awesome it would be if they could use this kind of automated reconciliation without having to change their current local payroll solutions, meaning that this kind of process automation is simply “plugged” into the existing local payroll? The good news is that with the new generation of global payroll solutions, this scenario is already a reality.

So what are you waiting for? Bring process automation into your global payroll, improving process efficiency, data quality, and ultimately employee empowerment in your business.


Marc-Oliver Fiedler

Marc-Oliver Fiedler

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