Clasification - ROC Curve with very high number of false negatives
Notice how the precision is very high and all of the other metrics are very low.  Now look at the class balance of your problem:

$$TP+FN=Actual Positive=31,245$$
$$TN+FP=Actual Negative=508$$

So your data is heavily skewed toward positives.  To have gotten a model that is producing this poorly, I think you may have provided the model with the precision as the cross validation metric.  The precision is a very bad cross validation metric in this case since it will result in poor accuracy and poor recall.  accuracy is also not a good metric as your model could classify everything as positive and get an accuracy of:

$$AC=\frac{31,245}{31,763}=.984$$

For cases like this where the classes are grossly weighted toward one value, I suggest using the F1-score as your cross validation metric.  The F1-score is the harmonic mean of precision and recall and hence balances these two factors nicely.  Wikipedia actually has a very nice explanation of classification metrics here and this paper is top notch if you even need to understand multi-class metrics and confusion matrices.

Hope this helps!