Title: Behavioural attitudes and financial performance: New ideas for segmenting bank customers
Authors: Caterina Liberati - University of Milano-Bicocca (Italy) [presenting]
Galina Andreeva - University of Edinburgh (United Kingdom)
Abstract: Credit scoring models are generally trained using customers credit history and demographics. Recent works in interdisciplinary studies have showed that alternative source of information, such as psychological traits or behavioural attitudes, can aid to improve default prediction. We would like to verify if an explorative segmentation based on financial knowledge, preferences and personality traits is able to detect different customer typologies also in terms of financial performance. This could be the case when the bank management has to deal with new clients or with those without a long banking history. The segmentation has been derived employing hierarchical clustering on factorial scores coming from non-linear Principal Component Analysis (PCA). The kernel-PCA allowed data to be mapped indirectly in a very-high-dimensional space $F$ where is simple to construct a hyperplane that divides the points into arbitrary clusters. The choice of the kernel functions and its parameters provided different kernel factors which produced, in turn, alternative clusters solutions. All the partitions have been ranked using alternative criteria that measure aspects of clusters validity.