We introduce a method for deriving a metric, locally based on the Fisher in
formation matrix, into the data space. A self-organizing map (SOM) is compu
ted in the new metric to explore financial statements of enterprises. The m
etric measures local distances in terms of changes in the distribution of a
n auxiliary random variable that reflects what is important in the data. In
this paper the variable indicates bankruptcy within the next few years. Th
e conditional density of the auxiliary variable is first estimated, and the
change in the estimate resulting from local displacements in the primary d
ata space is measured using the Fisher information matrix. When a self-orga
nizing map is computed in the new metric it still visualizes the data space
in a topology-preserving fashion, but represents the (local) directions in
which the probability of bankruptcy changes the most.