Mortgage Data Mining

		    George H. John  and  Ying Zhao
		Global Business Intelligence Solutions
		     IBM Almaden Research Center
		     {gjohn,zhao}@almaden.ibm.com
		  http://xenon.stanford.edu/~gjohn/


This paper reports a preliminary investigation of the use of modern
data mining tools for mortgage scoring.  Using IBM's Intelligent Miner
(a data mining toolbox), we built a model of serious delinquency on a
sample of data from Mortgage Information Corporation's Loan
Performance System, which contains over 20 million loans with a volume
of over $1.6 trillion.  Currently, two technologies prevail in
mortgage scoring: logistic regression, a very old and very simple
method, and neural networks, newer and more complex types of models
that can be extremely difficult to interpret.  The radial basis
function (RBF) algorithm in Intelligent Miner combines the
mathematical complexity and generality of neural networks with a
comprehensible visualization that explains the RBF model.  Due to the
performance and understandability of the RBF model, as well as other
unique technologies not described here, the Intelligent Miner should
be a useful tool for mortgage bankers, facilitating development of
customized systems for mortgage scoring and other mortgage banking
applications.

Citation:
George H. John and Ying Zhao. Mortgage data mining.  In _Computational
Intelligence in Financial Engineering_, pages 232--236, Piscataway,
NJ, March 1997. IEEE Press.