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

arXiv: https://arxiv.org/abs/2109.09425 · HTML: https://mlsanity.com/paper/2109.09425/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 20 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [LSTM: A Search Space Odyssey](https://mlsanity.com/paper/1503.04069/) (2015)
- [A survey on trajectory clustering analysis](https://mlsanity.com/paper/1802.06971/) (2018)
- [Intelligent Vector-based Customer Segmentation in the Banking Industry](https://mlsanity.com/paper/2012.11876/) (2020)

## Built on by (most cited first, 5 of 5)

- [Reinforcement Learning Your Way: Agent Characterization through Policy Regularization](https://mlsanity.com/paper/2201.10003/) (2022)
- [Can Interpretable Reinforcement Learning Manage Prosperity Your Way?](https://mlsanity.com/paper/2202.09064/) (2022)
- [Understanding Spending Behavior: Recurrent Neural Network Explanation and Interpretation](https://mlsanity.com/paper/2109.11871/) (2021)
- [Balancing Profit, Risk, and Sustainability for Portfolio Management](https://mlsanity.com/paper/2207.02134/) (2022)
- [Reinforcement Learning with Intrinsic Affinity for Personalized Prosperity Management](https://mlsanity.com/paper/2204.09218/) (2022)

## Abstract (arXiv)

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.