Interpreting the Model

The parameter estimates table summarizes the effect of each predictor. While interpretation of the coefficients in this model is difficult due to the nature of the link function, the signs of the coefficients for covariates and relative values of the coefficients for factor levels can give important insights into the effects of the predictors in the model.

For covariates, positive (negative) coefficients indicate positive (inverse) relationships between predictors and outcome. An increasing value of a covariate with a positive coefficient corresponds to an increasing probability of being in one of the "higher" cumulative outcome categories.
For factors, a factor level with a greater coefficient indicates a greater probability of being in one of the "higher" cumulative outcome categories. The sign of a coefficient for a factor level is dependent upon that factor level's effect relative to the reference category.

Parameter estimates table showing significance values of less that .05 for AGE and OTHNSTAL — Figure 1. Parameter estimates for model with Complementary log-log link

Having chosen the model with the Complementary log-log link, you can make some interpretations based on the parameter estimates.

The significance of the test for Age in years is less than 0.05, suggesting that its observed effect is not due to chance. Since its coefficient is positive, as age increases, so does the probability of being in one of the higher categories of account status.
By contrast, Duration in months adds little to the model.
While there is no single category of NUMCRED that is significant on its own, there are two that are marginally significant. Usually, it is worth keeping such a variable in the model, since the small effects of each category accumulate and provide useful information to the model. Interestingly, while those with one credit at the bank are more likely to be in the lower outcome categories than those with more credits, those with two or three credits are less likely to be in the lower outcome categories than those with four credits.
OTHNSTAL also seems to be an important predictor on empirical grounds. Those with some other installment debt are more likely to be in the lower outcome categories than those without.
On the other hand, HOUSNG doesn't seem to contribute to the model in a meaningful way and could probably be dropped without substantially worsening the model.