Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Latent Representations of Bank Customers With The Variational
  Autoencoder   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.

26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess