  The workflow of data scientists normally involves potentially inefficient
processes such as data mining, feature engineering and model selection. Recent
research has focused on automating this workflow, partly or in its entirety, to
improve productivity. We choose the former approach and in this paper share our
experience in designing the client2vec: an internal library to rapidly build
baselines for banking applications. Client2vec uses marginalized stacked
denoising autoencoders on current account transactions data to create vector
embeddings which represent the behaviors of our clients. These representations
can then be used in, and optimized against, a variety of tasks such as client
segmentation, profiling and targeting. Here we detail how we selected the
algorithmic machinery of client2vec and the data it works on and present
experimental results on several business cases.
