{"id":11246,"date":"2018-01-25T09:00:00","date_gmt":"2018-01-25T09:00:00","guid":{"rendered":"https:\/\/zapliance.com\/?p=11246"},"modified":"2022-08-26T13:53:25","modified_gmt":"2022-08-26T13:53:25","slug":"tracking-down-rogue-states-in-sap","status":"publish","type":"post","link":"https:\/\/zapliance.com\/en\/blog\/tracking-down-rogue-states-in-sap\/","title":{"rendered":"Tracking down rogue states in SAP"},"content":{"rendered":"\n

Having already identified the top 10 \u201crogue states\u201d in our last Blog Post, this week we can now go on to analyze whether any payments have actually been made to a bank account in one of those dubious states and if so to what extent. So without further ado \u2013 let\u2019s get down to business and get our hands dirty!<\/p>\n\n\n\n

The basics of \u201crogue states\u201d<\/h2>\n\n\n\n

Perhaps the term \u201crogue states\u201d is completely foreign to you and you are not really sure what it means? If so, I recommend you take a look at the blog post from last week<\/a> first. But for the more lazy readers out there, here\u2019s a quick recap:<\/p>\n\n\n\n

Various organizations deal with the classification of countries in terms of the extent of corruption. This is quite an extensive area, which is why the organizations have different priorities, such as money laundering, financing nuclear weapons or the like. For example, every year the non-governmental organization Transparency International publishes the Corruption Perception Index (CPI), a list of perceived levels of corruption. According to this index, the Scandinavian countries (Denmark, Norway, Sweden, Finland) are among the top 6 with the least perceived corruption. Germany is in 10th place, but it is really the other end of the list that we need to look at to serve as a basis for our analysis in SAP:<\/p>\n\n\n\n

Rank<\/th>Country<\/th>Score<\/th><\/tr>
166<\/td> Iraq<\/td>17<\/td><\/tr>
166<\/td> Venezuela<\/td>17<\/td><\/tr>
168<\/td> Guinea-Bissau<\/td>16<\/td><\/tr>
169<\/td> Afghanistan<\/td>15<\/td><\/tr>
170<\/td> Yemen<\/td>14<\/td><\/tr>
170<\/td> Libya<\/td>14<\/td><\/tr>
170<\/td> Sudan<\/td>14<\/td><\/tr>
174<\/td> North Korea<\/td>12<\/td><\/tr>
175<\/td> Southern Sudan<\/td>11<\/td><\/tr>
176<\/td> Somalia<\/td>10<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Now, of course, it depends on your company, your industry and your trade relations, but we would not normally want to be providing support for criminal activities in these countries with funds from our company. We have already identified the top 10 \u201crogue states\u201d. So now let\u2019s see how things look in SAP.<\/p>\n\n\n\n

Data basis for the evaluation of payments to \u201crogue states\u201d<\/h2>\n\n\n\n

Before we can carry out an analysis, we first need the relevant data for an evaluation. We obtain these from the SAP tables \u201cREGUP\u201d (Processed items from the payment program) and \u201cREGUH\u201d (Settlement data from payment program). The tables contain useful information, for example, on the bank code of the payee\u2019s bank, the paying company code, but also the country key of the corresponding bank (REGUH table, fields: ZBNKL, ZBUKR and ZBNKS) or the document number of the paid invoice (REGUP table, field: BELNR). In addition, we are only interested in the actual payments and want to ignore all trial payment runs. This is what the field \u201cXVORL\u201d is for in REGUP and REGUH. This gives us the information we need about the relevant bank data in SAP that was used for payments.<\/p>\n\n\n\n

However, we are also interested in the level of cash flows to dubious states. The relevant fields for the evaluation can also be found in the REGUP table:<\/p>\n\n\n\n