  The aim of this study is to demostrate that mobile phone usage data can be
used to make predictions and find the best classification method for credit
scoring even if the dataset is small (2,503 customers). We use different
classification algorithms to split customers into paying and non-paying ones
using mobile data, and then compare the predicted results with actual results.
There are several related works publicly accessible in which mobile data has
been used for credit scoring, but they are all based on a large dataset. Small
companies are unable to use datasets as large as those used by these related
papers, therefore these studies are of little use for them. In this paper we
try to argue that there is value in mobile phone usage data for credit scoring
even if the dataset is small. We found that with a dataset that consists of
mobile data based only on 2,503 customers, we can predict credit risk. The best
classification method gave us the result 0.62 AUC (area under the curve).
