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client2vec: Towards Systematic Baselines for Banking Applications

Leonardo Baldassini,Jose Antonio Rodríguez Serrano

Published 2018 in arXiv.org

ABSTRACT

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.

PUBLICATION RECORD

  • Publication year

    2018

  • Venue

    arXiv.org

  • Publication date

    2018-02-12

  • Fields of study

    Mathematics, Computer Science

  • Identifiers
    arXiv 1802.04198
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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