## 2 Background and Methodological Framework  
In recent years, artificial intelligence has become a tool which permits the handling of huge databases as well as the use of algorithms which, although complex in structure, provide results which may be interpreted easily. This framework offers the possibility of detecting and checking fiscal fraud which is an area that has aroused the interest of researchers, and generated concern for public administrative offices. In this paper, the proposal put forward focuses not only on the utilization of neural networks for detecting fiscal fraud as regards taxpayers in Spain, but also on contributing to precise fraud profiling to facilitate tax inspections. From the literature, data mining techniques present several possibilities for data processing aimed at fraud analysis [12]. Neural network models normally outperform other predictive linear and non-linear models where perfection and predictive capacity are concerned [13; 14; 15]. From the quantitative perspective, they often consist of optimum combinations which permit better prediction and more accurate estimations than occurs with other types of models. The neural network facilitates classification of each tax filer as fraudulent or not fraudulent, and furthermore, it reveals a taxpayer's likelihood to be fraudulent. In other words, it does not only classify individuals as prone to fraud or not, but also computes each filer's probability to commit fraud. Hence, tax filers are classified according to their propensity to commit fraud. Attaining the above-mentioned goal comes with a price due to software availability (suitable software for these techniques is not common), computation capacity (network algorithms are rather complicated and require adequate hardware for convergence), and methodological control (Machine Learning techniques are not trivial concerning methodology). To meet the objectives of this study, IBM software and hardware were used (www.ibm.es) to achieve the algorithm convergence of neural networks applied to millions of data and hundreds of variables. On the other hand, as any graphic representation which involves millions of points cannot be done without infrastructure which is appropriate for huge amounts of data, the same programming has been utilized. We can define an artificial neural network as an intelligent system capable not only of learning, but also of generalizing. A neural network is made up of processing units referred to as neurons and nodes. The nodes are organized in groups called \"layers\". Generally, there are three types of layers: An input layer, one or several hidden layers, and an output layer. Connections are established between the adjacent nodes of each layer. The input layer, whereby the data is presented to the network, is made up of input nodes which receive the information directly from outside. The output layer represents the response of the network to the inputs received by transferring the information out. The hidden or intermediate layers, located in between the input and output layers, process the information and are the only layers which have no connection to the outside. The most commonly found network structure is a type of network which is fed forwards or referred to as a feedforward network, since the connections established between neurons move in one direction only according to the following order: The input layer, hidden layer(s), and the output layer. For example, Figure 1 depicts a feedforward network with two hidden layers. Nevertheless, it is also possible to find feedback networks which have connections moving backwards, that is, from the nodes to the processing elements of previous layers, as well as recurrent networks with connections between neurons in the same layers, as well as a connection between the node and itself. Figure 2 illustrates a network model where the different types of mentioned connections can be found moving forwards, backwards, and also in a recurrent pattern, resulting in a fully interconnected network.