Independent Variable Importance
The importance of an independent variable is a measure of how much the network's model-predicted value changes for different values of the independent variable. Normalized importance is simply the importance values divided by the largest importance values and expressed as percentages.
The importance chart is simply a bar chart of the values in the importance table, sorted in descending value of importance. It appears that variables related to a customer's stability (employ, address) and debt (creddebt, debtinc) have the greatest effect on how the network classifies customers; what you cannot tell is the "direction" of the relationship between these variables and the predicted probability of default. You would guess that a larger amount of debt indicates a greater likelihood of default, but to be sure, you would need to use a model with more easily interpretable parameters.