Fuel laundering scam is prevalent in many countries. Typically, a case may concern 100 companies, several hundred people, and up to 100 thousand money transfers/invoices. Analysis of this amount of data is difficult even if it is stored in a database. To gain insight on the mechanism of the case we use in this work the extension of previously proposed ontology model, called the minimal model. The conceptual minimal model consists of eight layers of concepts, that are structured in order to use available data on facts to uncover relations. FuelFlowVis is an intelligent tool that supports continuous visual analytic process by exploiting the following features: navigation between global and local views, filters allowing displaying transactions by value, time, and type of goods. A user can inspect selected flows which give insight into crime patterns. We used the tool for 3 large Polish fuel laundering cases form the 2001–2003 period. For none of the cases we have complete data. We find that the methods to hide the proceeds of crime are very similar between the cases. The evidence as presented by prosecutors is of varied quality, and depends on the size of the crime group. In all the cases prosecutor's had an enormous problem to uncover money flows from the source of money (profit centre) to sinks (where the money leaves companies and goes as cash to organizers of the scheme). This occurs because the use of traditional analytic tools (spreadsheets or non-semantic visualization tools) cannot provide information about chains of transactions – a separate binary relations view does not provide complete insight to the case. Prospects on future reasoning capabilities of the tool will be presented.