Financial Fraud Detection Through Artificial Intelligence
The present work shows the analysis and modeling of a database with information about the various credit card transactions. The objective is to detect transactions that are fraudulent. In the modeling process, the �Ridge and Lasso�, �Boosting� and �Random Forest� techniques were applied...
| Main Authors: | Rodriguez-Aguilar, R., Marmolejo-Saucedo, J.A., Vasant, P., Litvinchev, I. |
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| Format: | Article |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.24808 / |
| Published: |
Springer Science and Business Media Deutschland GmbH
2020
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| Online Access: |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083433574&doi=10.1007%2f978-3-030-36178-5_6&partnerID=40&md5=1a839fe927d474f01394f11cc4836835 http://eprints.utp.edu.my/24808/ |
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| Summary: |
The present work shows the analysis and modeling of a database with information about the various credit card transactions. The objective is to detect transactions that are fraudulent. In the modeling process, the �Ridge and Lasso�, �Boosting� and �Random Forest� techniques were applied in the modeling and variables selection. The results show that the accuracy of the models was very high, so the metric �Recall� was chosen as a second criterion for selecting the best model. This metric measures the percentage of positive values of the variable �fraud�. It is concluded that the best model is that of �Boosting� with 1,500 trees and a K-Folds of 10 that presented the best results in both training and validation. © 2020, Springer Nature Switzerland AG. |
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