  Credit scoring is without a doubt one of the oldest applications of
analytics. In recent years, a multitude of sophisticated classification
techniques have been developed to improve the statistical performance of credit
scoring models. Instead of focusing on the techniques themselves, this paper
leverages alternative data sources to enhance both statistical and economic
model performance. The study demonstrates how including call networks, in the
context of positive credit information, as a new Big Data source has added
value in terms of profit by applying a profit measure and profit-based feature
selection. A unique combination of datasets, including call-detail records,
credit and debit account information of customers is used to create scorecards
for credit card applicants. Call-detail records are used to build call networks
and advanced social network analytics techniques are applied to propagate
influence from prior defaulters throughout the network to produce influence
scores. The results show that combining call-detail records with traditional
data in credit scoring models significantly increases their performance when
measured in AUC. In terms of profit, the best model is the one built with only
calling behavior features. In addition, the calling behavior features are the
most predictive in other models, both in terms of statistical and economic
performance. The results have an impact in terms of ethical use of call-detail
records, regulatory implications, financial inclusion, as well as data sharing
and privacy.
