1. A method for detecting unknown scenarios in a data processing system, the method comprising the steps of: storing in a database known scenario data describing one or more known scenarios, said known scenario data describing how actors in a system model act and interact; using a processor device performing: A) splitting the known scenarios into elements, each element relating to at least one of: an actor a, and an action x of said actor; B) receiving an assumption describing an end state in which a possible scenario can result in order to assess a potential for fraud, wherein said assumption is not an end state of a known scenario in the system model; C) beginning with the assumption as a root node, constructing a scenario tree by: 1) assigning each element to a node; 2) generating at least one path comprising one or more nodes associated from said root node, wherein each path in the scenario tree represents a different possibility to reach the end state of the possible scenario according to the assumption by: a) at a current path in the scenario tree, selecting a node associated with an actor a; b) adding the selected node a to the current path, wherein said node a can be added to multiple paths; c) repeatedly performing the following steps for each actor a in the system model for each different possibility to reach the end state: i) selecting at least one intermediate node representing an action x of the node a; ii) adding the at least one intermediate node to the scenario tree; and iii) adding at least one node representing a requirement necessary for the actor a to perform the action x specified in the at least one intermediate node; d) interconnecting the nodes of each path in the scenario tree to form a possible scenario; e) comparing each possible scenario to the known scenarios stored in the database; f) eliminating the known scenarios from the tree, leaving the unknown scenarios; and g) identifying the unknown scenarios as new scenarios in order to assess their potential for fraud.