Machine learning and spending patterns

The aim of this study is to research the possibility of using customer transactional data to identify spending patterns among individuals, that in turn can be used to assess creditworthiness. Two different approaches to unsupervised clustering are used and compared in the study, one being K-means and the other an hierarchical approach. The features used in both clustering techniques are extracted from customer transactional data collected from the customers banks. Internal cluster validity indices and credit scores, calculated by credit institutes, are used to evaluate the results of the clustering techniques. Based on the experiments in this report, we believe that the approach exhibit interesting results and that further research with evaluation on a larger dataset is desired. Proposed future work is to append additional features to the models and study the effect on the resulting clusters.