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Fraud Prediction in Smart Supply Chains Using Machine Learning Techniques

  • Fabián-Vinicio Constante-Nicolalde
  • Paulo Guerra-Terán
  • Jorge-Luis Pérez-MedinaEmail author
Conference paper
  • 55 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1194)

Abstract

In the domain of Big Data, the company’s supply chain has a very high-risk exposure and this must be observed from a preventive perspective, that is, act before such situations occur. As a company grows and diversifies the number of suppliers, customers and therefore increases its number of daily transactions and associated risks. Despite the innovation and improvements that have been incorporated into financial management, credit and debit cards are the main means of exchanging cash online, with the expansion of e-commerce, online shopping has also increased number of extortion cases that have been identified and that continues to expand greatly. It takes a lot of time, effort and investment to restore the impact of these damages. In this paper, we work with machine learning techniques, used in predicting smart supply chain fraud, are valuable for estimating, classifying whether a transaction is normal or fraudulent, and mitigating future dangers.

Keywords

Big Data Analysis Classification approaches Fraud prediction 

Notes

Acknowledgements

This work was made possible thanks to the financial support of “Universidad de Las Américas” from Ecuador and thanks to the participation of Polytechnic Institute of Leiria from Portugal.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Intelligent and Interactive Systems Lab (SI² Lab)Universidad de Las Américas (UDLA)QuitoEcuador
  2. 2.School of Technology and ManagementPolytechnic Institute of LeiriaLeiriaPortugal

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