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Investigating Attribute Assessment for Credit Granting on a Brazilian Retail Enterprise

  • Strauss Carvalho Cunha
  • Emanuel Mineda Carneiro
  • Lineu Fernando Stege Mialaret
  • Luiz Alberto Vieira Dias
  • Adilson Marques da Cunha
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 738)

Abstract

In this article, we investigate which features are required to enhance a credit scoring model for a Brazilian retail enterprise. In order to find attributes that can improve the performance of classifier algorithms for credit granting, a national and an international survey were carried out. A logistic regression classifier was used and the main result has improved the performance of data mining classifiers. The main contribution of this article was the verification that additional financial and behavioral data increase defaulting prediction performance on credit granting.

Keywords

Credit granting Attribute assessment Classifier algorithms Logistic regression Receiver operating characteristic (ROC) 

Notes

Acknowledgements

The authors would like to thank: the Brazilian Aeronautics Institute of Technology (ITA); the Casimiro Montenegro Filho Foundation (FCMF); the Software Engineering Research Group (GPES) members; and the 2RP Net Enterprise for their infrastructure, assistance, advice, data set, and financial support for this work.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Strauss Carvalho Cunha
    • 1
  • Emanuel Mineda Carneiro
    • 2
  • Lineu Fernando Stege Mialaret
    • 3
  • Luiz Alberto Vieira Dias
    • 2
  • Adilson Marques da Cunha
    • 2
  1. 1.Brazilian Federal Service of Data Processing - SERPROBrasiliaBrazil
  2. 2.Federal Institute of Education, Science and Technology of Sao Paulo - IFSPJacareiBrazil
  3. 3.Computer Science DepartmentBrazilian Aeronautics Institute of Technology - ITASao Jose dos CamposBrazil

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