Learning Latent Representations of Bank Customers With The Variational Autoencoder
This work addresses credit risk and customer management for retail banks, but it is incremental as it applies an existing method (VAE) with modifications to a specific domain.
The paper tackled the problem of learning customer creditworthiness representations for banking applications by using a Variational Autoencoder (VAE) to steer latent representations with Weight of Evidence, resulting in well-defined clusters suitable for marketing, risk assessment, and other bank activities.
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.