The issues pointed out in the various footnotes and references here do not seem to address this issue. There are numerous situations where detailed intelligent binning is not only appropriate, but adds value to the model.
Let's break it down to the basics, which is that in a digital world every thing is categorical. We never measure AGE down to the second, minute, day, week or even month. Why not? Because we assume that at those minute intervals the response variable is the same. How is tat different from assuming that the response variable is the same for those between ages of 25 and 27, If the data shows that the credit risk for people with 10-15 years of credit history is the same, why would we assume there is a linear relationship there?
Isn't that just trying to impute more in the data than exists?
It is true that by discretizing the data we increase the available degrees of freedom, but only if we assume each interval is defined by a separate variable AND with large data sets (thousands of observations are not only common, but on the low side of many data sets). I think the problem with many comments here are from areas where sample sizes are small, biomed, social sciences, ... In the marketing and financial and other consumer worlds there is more data than you can shake a stick at.
Finally, binning has been an accepted and proven practice in the consumer industry since Fair, Isaac first started building scorecards, back in the 1960's. FICO still uses complex binning techniques for almost all of their models today. One of the current top data mining tools, TreeNet from Salford, is essentially based on binning techniques.
So, anyone who considers binning to not be best practice as a potential transformative technique is not behind the technology curve, way behind.