  Call Detail Record (CDR) datasets provide enough information about personal
interactions to support building and analyzing detailed empirical social
networks. We take one such dataset and describe the various ways of using it to
create a true social network in spite of the highly noisy data source. We use
the resulting network to predict each individual's likelihood to default on
payments for the network services, a complex behavior that involves a
combination of social, economic, and legal considerations. We use a large
number of features extracted from the network to build a model for predicting
which users will default. By analyzing the relative contributions of features,
we choose their best performing subsets ranging in size from small to medium.
Features based on the number of close ties maintained by a user performed
better than those derived from user's geographical location. The paper
contributions include systematic impact analysis that the number of calls
cutoff has on the properties of the network derived from CDR, and a methodology
for building complex behavior models by creating very large sets of diverse
features and systematically choosing those which perform best for the final
model.
