Risk insolvency predictive model MEU
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It is clear how, for the exposed ratio, the distribution of the bankrupt companies (in green colour) is decreasing to growing ratio (in terms of value); analogous but opposite considerations can be made in analysing the distribution of the companies known health (in yellow colour); consequently the course of the default frequency is strongly monotone decreasing. Figure 4
Ratio with a low predictive power
It is clear how, for the exposed ratio, the distribution of the bankrupt companies (in green colour) is similar to the distribution of the companies known to be healthy (in yellow colour); in fact the default frequency value is around 0.5, it means that the ratio does not have a predictive power, because they do not discriminate between healthy and bankrupt companies.
5.5.3 Self organising maps The visualisation of the interaction between financial ratios and default is one important point for improving the performance of credit risk estimation. The financial analyst can obtain considerable information about the direct control of the relation between the input and output parameter of the credit rating model. This is an important field in data mining and different methodologies have been developed: clustering, Principal Component Analysis and Self-Organising Maps (SOMs). The SOM (Kohonen, 2001) is an unsupervised neural network algorithm that projects high-dimensional data onto a two-dimensional map. The projection preserves the topology of the data so that similar data items will be mapped to nearby locations on the map. This allows the user to identify clusters, that is, large groupings of a certain type of input pattern. Further examination may then reveal what features; the members of a cluster have in common. Since its invention by Professor Teuvo Kohonen in the early 1980s (Kohonen, 1982), more than 4000 research papers have been published on the algorithm (Ojia et al., 2002), its visualisation and application. The maps comprehensively