A carregar...
Publicação
Fraud Prediction in Smart Supply Chains Using Machine Learning Techniques
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 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. | 158.92 KB | Adobe PDF |
Orientador(es)
Resumo(s)
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.
Descrição
EISBN - 9783030425203
Site: https://redi.cedia.edu.ec/document/251353
Site: https://redi.cedia.edu.ec/document/251353
Palavras-chave
Fraud prediction Classification approaches Big Data Analysis
Contexto Educativo
Citação
Constante-Nicolalde, FV., Guerra-Terán, P., Pérez-Medina, JL. (2020). Fraud Prediction in Smart Supply Chains Using Machine Learning Techniques. In: Botto-Tobar, M., Zambrano Vizuete, M., Torres-Carrión, P., Montes León, S., Pizarro Vásquez, G., Durakovic, B. (eds) Applied Technologies. ICAT 2019. Communications in Computer and Information Science, vol 1194. Springer, Cham. https://doi.org/10.1007/978-3-030-42520-3_12.
Editora
Springer Nature
Coleções
Licença CC
Sem licença CC