Part X of the series: “Digitization of auditing SAP purchasing processes”
Data and process analytics have the potential to bring great benefit to auditing. Nonetheless the questions are: how is it possible to pursue and analyze the enormous and extensive results? Does one have to pursue each and every case that emerges from data analytics? Often, there are way too many results and false positives! Frequently auditors are overwhelmed by the amount of results. In the following, I would like to introduce an approach on a possible way forward for Professional Judgement in Purchase Audits.
- Digitization of auditing SAP purchase processes
- How process auditing is transformed through digitization
- Automated auditing of SAP master data
- Auditing of purchase orders and goods received
- Invoice auditing in SAP
- Just pay it twice: auditing payments in SAP
- The search for exotic processes
- Segregation of Duties in the SAP purchase process
- SAP data structure for the purchase to payables process
- The end of digitization - professional judgement in procurement
Before you proceed reading on the details of the indicators, I would recommend to read the concept of indicators in part 3 of the series first.
Divide et Impera
"Divide and rule" – says the Latin scholar
This simply means: when a task is too difficult, one can divide it into subgroups and deal with the easier subgroups until all of them have been processed.
How does this affect the interpretation of data analysis results by the auditor?
In total I have implemented 125 indicators for process weaknesses(about every process: purchase, order to cash, fixed assets and inventory, as well as cross process indicators). In the process of purchase to payables are currently 45 indicators. You can download the details about all purchase indicators here.
During the implementation, the described problem of too many results arose and because of that a solution needed to be constructed.
I will elaborate this on the following example: let’s take the following indicator:
Vendor master data with inappropriate or incomplete payment terms
This indicator is associated with the audit objective of saving opportunities.
There is a risk that inefficiencies or working capital losses were caused by loose payment management.
The criterion for this indicator is:
The receipt will be marked as the payment terms of the referenced vendor are not maintained or are inadequate.
Now imagine 2541 documents being affected.
When looking at this number, some auditors regret having this all started.
How to pursue 2541 indications?
To handle this problem, I have developed the concept of “profiling” which has been implemented consistently in zap Audit. It’s pretty simple: every document which was marked with an indicator is automatically sorted and allocated to a certain group. The creation of groups is dependant on the indicator, as well as the specific analysis objective.
The idea behind it is that one only has to look at the result groups and not at every single document.
Let us pursue this example with the indicator "vendor master data with inappropriate or incomplete payment terms.” This indicator creates its profiles according to the “vendor” criterium, which means that all documents with the same vendor are allocated to a profile, a result group. For example, the 2541 documents are allocated to 5 profiles with 5 different vendors.
Afterwards it's sufficient to investigate on why these 5 vendors had a problem with the payment terms. The 2541 documents do not have to be consulted individually and there is no need for random sampling because the profiles insightfully categorize the 2541 documents.
As a result, not a single case but case groups have to be audited, which makes auditing of SAP processes much more efficient and leads to a much better understanding of the audited unit.
In the above-mentioned example, the question that needs to be answered is:
Why does vendor X have a problem with the payment terms?
... and thus auditing the indicator is completed.
Would you like to try out the zap Audit indicators and audit approach for free? Schedule an appointment now:
About the author:
Prof. Dr. Nick Gehrke is a member of the board of the Artificial Intelligence Centre Hamburg, and a professor and course leader at the Nordakademie Hamburg, and Co-Founder and Data Scientist at zapliance. He started zapliance after meeting Alexander Rühle at PWC and set their eyes on the same goal: Changing the way business professionals of the future work with data.