  Understanding customer behavior is fundamental for many use-cases in
industry, especially in accelerated growth areas such as fin-tech and
e-commerce. Structured data are often expensive, time-consuming and inadequate
to analyze and study complex customer behaviors. In this paper, we propose a
multi-graph embedding approach for creating a non-linear representation of
customers in order to have a better knowledge of their characteristics without
having any prior information about their financial status or their interests.
By applying the current method we are able to predict users' future behavior
with a reasonably high accuracy only by having the information of their
friendship network. Potential applications include recommendation systems and
credit risk forecasting.
