In the first of the three articles dealing with GRC platforms, I have described substantive and technical challenges in the area of auditing and audit management. In today's second part, we deal with functionalities that audit management platforms should have. Some of these functionalities can be found in this blog post. However, they do not represent a complete catalogue, but are only an excerpt without claiming to be complete.
Although it may be true for the headline, this blog post is not intended to be about ransomware - also called encryption trojans. It's about data analysis and how crucial the "last meters" can be. Sometimes it's the little things in life that determine the success or failure of a project. This also applies to data analyses where the data comes from the SAP system and the results are to be checked in the system. Although the results and raw data come from the same system, it is often very time-consuming to carry out the check in the system. In this blog post, I will introduce our approach and show you the advantages of dab:Link.
Internal controls are often considered to only be the responsibility of finance and audit professionals. But if internal controls work in harmony, across an organization and the Three Lines of Defense, they can help the organization avoid legal repercussions and run more effectively and efficiently. While creating a rigorous internal control system can be challenging, it’s definitely possible.
Now to specific examples of use. I will show you three examples from our portfolio to give you some ideas and have divided them into three different categories. Firstly, I will use the example of a traditional duplicate payments analysis to explain how known, transaction-based analyses can be improved. I will then describe new analytical approaches, based on the specific example of a comparison of master data quality expressed as an objectively determined indicator. The third example shows the segmenting of business partners in financial accounting – not in the traditional way but based on the posting structure exhibited by these partners.
Digitisation has been a burning issue in companies not only since Corona, but the sudden switch to decentralised locations such as the home office clearly shows the weak points of digitisation. Any omissions or lack of readiness now have a strong impact on the daily work routine in each company.
The analysis of processes in companies with the help of process mining, especially for the processes purchase-to-pay and order-to-cash, is still in great demand. We have already presented our position and solutions on this topic in various blog posts.
In this blog post we show you two examples of methods by which the analysis software “ACL™ Robotics” - previously known as “ACL™ Analytics” - of the software manufacturer Galvanize makes it possible to implement machine learning. For expert users: Both supervised and unsupervised learning approaches are supported. “ACL™ Robotics” is a software solution which has already been assisting the manual and automated analysis of large amounts of data for many years. Besides having a variety of interfaces to e.g. SAP (via “SAP Connector”), Salesforce, Google Hive, Amazon Redshift, Outlook, PDF imports or any ODBC data sources desired, an automated script language helps to automate the sequence of analytic steps. The software developer Galvanize allocates this to the field of RPA (Robotic Process Automation). Individual analytic steps are implemented by methods or commands, such as sorting, summarizing, joining and relating to name only a few. With Version 14 these analytic commands have been extended by three machine learning commands named as “Train”, “Predict” and “Cluster”. In this blog post we are familiarising you with the use of these three commands, taking specific examples from the world of business, such as forecasting return values and the clustering of customers combined with due dates for payment. For existing ACL users, we also offer the opportunity to download ACL projects with the examples, and thus be able to try out each method, step by step, for yourself.
Process Mining is one of the trending topics in many companies, as it is hoped that by visualizing the process, weak points can be identified and eliminated. However, very few people realize that process mining does not work without the correct preparation of data.
Those of you who are responsible for data analysis in your company will certainly have already noticed that it is one thing to run analyses. The processing of the results, on the other hand, is a completely different field, which usually takes much more time and effort than expected.