  Micro-segmentation of customers in the finance sector is a non-trivial task
and has been an atypical omission from recent scientific literature. Where
traditional segmentation classifies customers based on coarse features such as
demographics, micro-segmentation depicts more nuanced differences between
individuals, bringing forth several advantages including the potential for
improved personalization in financial services. AI and representation learning
offer a unique opportunity to solve the problem of micro-segmentation. Although
ubiquitous in many industries, the proliferation of AI in sensitive industries
such as finance has become contingent on the explainability of deep models. We
had previously solved the micro-segmentation problem by extracting temporal
features from the state space of a recurrent neural network (RNN). However, due
to the inherent opacity of RNNs our solution lacked an explanation. In this
study, we address this issue by extracting a symbolic explanation for our model
and providing an interpretation of our temporal features. For the explanation,
we use a linear regression model to reconstruct the features in the state space
with high fidelity. We show that our linear regression coefficients have not
only learned the rules used to recreate the features, but have also learned the
relationships that were not directly evident in the raw data. Finally, we
propose a novel method to interpret the dynamics of the state space by using
the principles of inverse regression and dynamical systems to locate and label
a set of attractors.
