Behavioral attitudes and financial performances: New ideas for a banking segmentation

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. In our work, 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 credit 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 Factors coming from non-linear Principal Component Analysis (PCA). The Kernel-PCA allowed to map data 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.