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Micro-segmentation of customers in the finance sector is a non-trivial taskand has been an atypical omission from recent scientific literature. Wheretraditional segmentation classifies customers based on coarse features such asdemographics, micro-segmentation depicts more nuanced differences betweenindividuals, bringing forth several advantages including the potential forimproved personalization in financial services. AI and representation learningoffer a unique opportunity to solve the problem of micro-segmentation. Althoughubiquitous in many industries, the proliferation of AI in sensitive industriessuch as finance has become contingent on the explainability of deep models. Wehad previously solved the micro-segmentation problem by extracting temporalfeatures from the state space of a recurrent neural network (RNN). However, dueto the inherent opacity of RNNs our solution lacked an explanation. In thisstudy, we address this issue by extracting a symbolic explanation for our modeland providing an interpretation of our temporal features. For the explanation,we use a linear regression model to reconstruct the features in the state spacewith high fidelity. We show that our linear regression coefficients have notonly learned the rules used to recreate the features, but have also learned therelationships that were not directly evident in the raw data. Finally, wepropose a novel method to interpret the dynamics of the state space by usingthe principles of inverse regression and dynamical systems to locate and labela set of attractors.

Understanding Spending Behavior: Recurrent Neural Network Explanation and Interpretation