Yes - you should be able to use Area Under the Receiver Operating Curve (AUC) with SVM classification. Many classification algorithms return (or can return) probability values instead of discrete classifications, and the results are evaluated with AUC. Probabilities can then be converted to class values based on a threshold value (e.g., Prob > 0.5 = Presence; Prob <=0.5 = Absence), and this can be done based on AUC output (e.g., finding the point where Sensitivity=Specificity). There are lots of examples in the Species Distribution Modeling literature, among other fields.
If you're using R a couple of packages that might be helpful are Presence Absence and ROCR.
And yes - I it is common that as you go further from the decision boundaries, you'll have more accurate classification - that's because along that boundary it might difficult to differentiate classes (e.g., if you have a gradient from white to black, it will be tough to classify a gray color near the middle of the spectrum as one or the other). Given this, there are lots of ways to set the threshold value, and some are given in the aforementioned Presence Absence package. Also, there's a table I find useful in this book, which summarizes some threshold options (sorry - don't have the page or chapter handy). A search for "Threshold probabilities classification" and similar seems to turn up some informative results.
Hope that helps!