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...

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Main Authors: Rodriguez-Aguilar, R., Marmolejo-Saucedo, J.A., Vasant, P., Litvinchev, I.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.24808 /
Published: Springer Science and Business Media Deutschland GmbH 2020
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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spelling utp-eprints.248082021-08-27T06:15:32Z Financial Fraud Detection Through Artificial Intelligence Rodriguez-Aguilar, R. Marmolejo-Saucedo, J.A. Vasant, P. Litvinchev, I. 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. Springer Science and Business Media Deutschland GmbH 2020 Article NonPeerReviewed 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 Rodriguez-Aguilar, R. and Marmolejo-Saucedo, J.A. and Vasant, P. and Litvinchev, I. (2020) Financial Fraud Detection Through Artificial Intelligence. Lecture Notes on Data Engineering and Communications Technologies, 43 . pp. 57-72. http://eprints.utp.edu.my/24808/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description 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.
format Article
author Rodriguez-Aguilar, R.
Marmolejo-Saucedo, J.A.
Vasant, P.
Litvinchev, I.
spellingShingle Rodriguez-Aguilar, R.
Marmolejo-Saucedo, J.A.
Vasant, P.
Litvinchev, I.
Financial Fraud Detection Through Artificial Intelligence
author_sort Rodriguez-Aguilar, R.
title Financial Fraud Detection Through Artificial Intelligence
title_short Financial Fraud Detection Through Artificial Intelligence
title_full Financial Fraud Detection Through Artificial Intelligence
title_fullStr Financial Fraud Detection Through Artificial Intelligence
title_full_unstemmed Financial Fraud Detection Through Artificial Intelligence
title_sort financial fraud detection through artificial intelligence
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2020
url 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/
_version_ 1741196871817232384
score 11.62408

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