Clustering in Recurrent Neural Networks for Micro-Segmentation using Spending Personality

Abstract

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.

Can Interpretable Reinforcement Learning Manage Prosperity Your Way?

“…This is consistent with the high affinity of conscientious spenders towards residential mortgages [17]. It is compelling to expand the notion of personality traits from spending to wealth creation, i.e., to base personal investment advice on historical spending behaviour [18,19].…”

Reinforcement Learning with Intrinsic Affinity for Personalized Prosperity Management

“…We have previously developed a three-node RNN that predicts customer personalities from an input vector of their classified financial transactions [3]. This input vector consists of six annual time steps, each consisting of 97 transaction classes; the values in each time step add up to one and are the fraction of a customer's annual spending per transaction category.…”

“…Although they achieved only a modest predictive accuracy, a subsequent study found that spending patterns over time expose salient information that is obscured in non-temporal form [13]; the authors in this study used the same personality model, but added temporal patterns such as variability of the amount, persistence of the category in time, and burstiness-the intermittent changes in frequency of an event. Recurrent neural networks (RNNs) are able to extract this salient information when predicting personality traits from financial transactions [3]. In, [14], we gained an understanding of these extracted features by interpreting the dynamics of the RNN state space through a set of attractors.…”

“…Effective customer engagement is a requisite for modern financial service providers that are adopting advanced methods to increase the level of personalization of their services [1]. Although artificial intelligence (AI) has become a ubiquitous tool in financial technology [2], research in the field has yet to significantly advance levels of personalization [3]. Asset management is an active research topic in AI for finance; however, the research opportunities presented by the need for personalized services are usually neglected [4].…”

Reinforcement Learning Your Way: Agent Characterization through Policy Regularization

“…We conclude that our fundamentally sound algorithm was able to imbue specific characteristic behaviours into our agents' policies. In future work, we intend to use this algorithm to develop a set of financial advisors that will optimize individual customers' investment portfolios according to their individual spending personalities [24]. While maximising portfolio values, these agents may prefer, e.g., property investments over crypto currencies which are analogous to right turns and left turns in our toy problem.…”