Hey Vernon,
The bottom line is the data set just does not have great predictors. I think the point of the example is just to show how to use the tools, not to show off a perfect data set.
Typically finding a good model does require, usually, some trial and error with manual adjustments to bins, and re-fitting the logistic model. The acceptable levels for the validation statistics also vary depending on the source, and depending on the final use of the model. Overall, I agree that the model in the case study isn't great.
One thing to note is that an AUROC too close to 1 may actually be considered "suspicious". Sometimes very high AUROC's come from predictors that know about the future, information that implicitly or explicitly knows whether a customer defaulted or not. If that predictor is a score from a different source, that may be acceptable. But in other cases it may be a predictor that should not be allowed in the model.
Hope this helps,
-Adam
