Data Science in Audit - Payroll Audit
Originally published: 04/07/2019 14:28
Last version published: 27/01/2021 08:49
Publication number: ELQ-84321-3
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Data Science in Audit - Payroll Audit

Work paper to test reasonablness of Bonus payment compare to Salary and any indication of fraud.

Testing entire payroll is practically impossible for auditors. This working paper will provide an opportunity for the auditors to visualize entire payroll i.e. salary vs bonuses in one graph to find outliers.

The issues this working paper could highlight is where employees receiving large bonuses compare to their salaries, that means benefiting certain employees with large bonus payments rather than having a uniform or bona fide bonus policy.

Further, this tool could be used in other areas such as
. Salary vs Overtime
. Finding employees that do not exist but receiving salaries etc. this could be achieved by comparing salary with attendance records/ number of time sheets for a period or system log in record compared to salary. The outliers would be no time sheet or no PC log in the period, but salary received.

The example used in explanatory video is real life example of Enron energy with real payroll data.

The aim of this series is to provide ready to use analytical working paper libraries to auditors and risk professionals to add value and improve audit quality. This would increase their level of comfort on providing opinion on financial statements numbers with incurring much additional time and effort.

Feel free to let me know if you have any queries.

This Best Practice includes
1 Python Script and 1 Excel Data File as an example and 1 png image file

Rizwan Ahmed Surhio offers you this Best Practice for free!

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Further information

To add value audit value

Python Libraries, Jupyter Notebook, clean data format, and files with no empty cells, using exactly same header names in the scripts as in the excel file.

empty cells, numbers that are not a number in data files. May not work without Jupyter Notebook

4.6 / 5 (7 votes)

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