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This paper attempts to enhance the visualization and extraction of information on the Self-Organizing Financial Stability Map (SOFSM). The SOFSM uses the Self-organizing map to represent a multidimensional financial stability space on a two-dimensional grid and allows monitoring the financial stability cycle represented by four states. We enhance visualization and information extraction of the SOFSM by the means of four tasks: (1) fuzzification of the map, (2) probabilistic modeling of state transitions, (3) contagion analysis and (4) outlier detection. The usefulness of the extensions is shown with sample visualizations and predictive performance.
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