# Learning Latent Representations of Bank Customers With The Variational Autoencoder

## TL;DR

This paper demonstrates how Variational Autoencoders can learn meaningful latent representations of bank customers, capturing creditworthiness and enabling effective clustering for banking applications.

## Contribution

It introduces a method to steer VAE latent spaces using Weight of Evidence, creating clusters that reflect customer creditworthiness, scalable to large, complex datasets.

## Key findings

- Latent space clustering aligns with customer creditworthiness.
- Method generalizes to new customers and high-dimensional data.
- Clusters are useful for banking activities.

## Abstract

Learning data representations that reflect the customers' creditworthiness can improve marketing campaigns, customer relationship management, data and process management or the credit risk assessment in retail banks. In this research, we adopt the Variational Autoencoder (VAE), which has the ability to learn latent representations that contain useful information. We show that it is possible to steer the latent representations in the latent space of the VAE using the Weight of Evidence and forming a specific grouping of the data that reflects the customers' creditworthiness. Our proposed method learns a latent representation of the data, which shows a well-defied clustering structure capturing the customers' creditworthiness. These clusters are well suited for the aforementioned banks' activities. Further, our methodology generalizes to new customers, captures high-dimensional and complex financial data, and scales to large data sets.