# Learning Latent Representations of Bank Customers With The Variational Autoencoder

arXiv: https://arxiv.org/abs/1903.06580 · HTML: https://mlsanity.com/paper/1903.06580/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2019.

## Built on by (most cited first, 4 of 4)

- [Multimodal Banking Dataset: Understanding Client Needs through Event Sequences](https://mlsanity.com/paper/2409.17587/) (2024)
- [Learning Transactions Representations for Information Management in Banks: Mastering Local, Global, and External Knowledge](https://mlsanity.com/paper/2404.02047/) (2024)
- [Bagging Supervised Autoencoder Classifier for Credit Scoring](https://mlsanity.com/paper/2108.07800/) (2021)
- [Leveraging AI and NLP for Bank Marketing: A Systematic Review and Gap Analysis](https://mlsanity.com/paper/2411.14463/) (2024)

## Abstract (arXiv)

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.