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MobiScore: Towards Universal Credit Scoring from Mobile Phone Data

Part of the Lecture Notes in Computer Science book series (LNISA,volume 9146)

Abstract

Credit is a widely used tool to finance personal and corporate projects. The risk of default has motivated lenders to use a credit scoring system, which helps them make more efficient decisions about whom to extend credit. Credit scores serve as a financial user model, and have been traditionally computed from the user’s past financial history. As a result, people without any prior financial history might be excluded from the credit system. In this paper we present MobiScore, an approach to build a model of the user’s financial risk from mobile phone usage data, which previous work has shown to convey information about e.g. personality and socioeconomic status. MobiScore could replace traditional credit scores when no financial history is available, providing credit access to currently excluded population sectors, or be used as a complementary source of information to improve traditional finance-based scores. We validate the proposed approach using real data from a telecommunications operator and a financial institution in a Latin American country, resulting in an accurate model of default comparable to traditional credit scoring techniques.

Keywords

  • Mobile Phone
  • Latin American Country
  • Customer Relation Management
  • Mobile Phone Usage
  • Mobile Phone Data

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Correspondence to Jose San Pedro .

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Pedro, J.S., Proserpio, D., Oliver, N. (2015). MobiScore: Towards Universal Credit Scoring from Mobile Phone Data. In: Ricci, F., Bontcheva, K., Conlan, O., Lawless, S. (eds) User Modeling, Adaptation and Personalization. UMAP 2015. Lecture Notes in Computer Science(), vol 9146. Springer, Cham. https://doi.org/10.1007/978-3-319-20267-9_16

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  • DOI: https://doi.org/10.1007/978-3-319-20267-9_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20266-2

  • Online ISBN: 978-3-319-20267-9

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