Clustering in Recurrent Neural Networks for Micro-Segmentation using
  Spending Personality
  Customer segmentation has long been a productive field in banking. However,
with new approaches to traditional problems come new opportunities.
Fine-grained customer segments are notoriously elusive and one method of
obtaining them is through feature extraction. It is possible to assign
coefficients of standard personality traits to financial transaction classes
aggregated over time. However, we have found that the clusters formed are not
sufficiently discriminatory for micro-segmentation. In a novel approach, we
extract temporal features with continuous values from the hidden states of
neural networks predicting customers' spending personality from their financial
transactions. We consider both temporal and non-sequential models, using long
short-term memory (LSTM) and feed-forward neural networks, respectively. We
found that recurrent neural networks produce micro-segments where feed-forward
networks produce only coarse segments. Finally, we show that classification
using these extracted features performs at least as well as bespoke models on
two common metrics, namely loan default rate and customer liquidity index.
