Bidirectional gated recurrent unit for improving classification in credit card fraud detection
In recent years, the use of credit cards around the world has grown enormously. Thus, the number of fraud cases have also increased, resulting in losses of thousands of dollars worldwide. Therefore, it is mandatory to use techniques that are able to assist in the detection of credit card fraud. For...
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| Main Authors: | , , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2021-03-01.
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| Online Access: | Get fulltext |
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| LEADER | 02241 am a22003133u 4500 | ||
|---|---|---|---|
| 001 | ijeecs22847_14742 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Sadgali, Imane |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Sael, Nawal |e author |
| 700 | 1 | 0 | |a Benabbou, Fouazia |e author |
| 245 | 0 | 0 | |a Bidirectional gated recurrent unit for improving classification in credit card fraud detection |
| 260 | |b Institute of Advanced Engineering and Science, |c 2021-03-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/22847 | ||
| 520 | |a In recent years, the use of credit cards around the world has grown enormously. Thus, the number of fraud cases have also increased, resulting in losses of thousands of dollars worldwide. Therefore, it is mandatory to use techniques that are able to assist in the detection of credit card fraud. For this purpose, we have proposed a multi-level architecture, composed of four levels: authentication level, behavioral level, smart level and background processing level. In this paper, we focus on the implementation of the smart level. The aim of this level is to develop a classifier for the detection of credit card fraud, using bidirectional gated recurrent units (BGRU). The experiments, applied on well-known credit card fraud dataset from Kaggle, show that this model has peak performance compared to other proposed models, with 97.16% for accuracy rate and 99.66% for the area under the ROC curve. | ||
| 540 | |a Copyright (c) 2020 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Bidirectional gated recurrent unit; Credit card fraud; Deep learning; Fraud detection; Machine-learning | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 21, No 3: March 2021; 1704-1712 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v21.i3 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/22847/14742 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/22847/14742 |z Get fulltext |