INTERNATIONAL CONFERENCE ON SPECTACULAR PROGRESSIVE IMPROVEMENTS IN ENGINEERING SCIENCES & COMPUTING (ICSPEC 2021)INTERNATIONAL CONFERENCE ON SPECTACULAR PROGRESSIVE IMPROVEMENTS IN ENGINEERING SCIENCES & COMPUTING (ICSPEC 2021)

The exceptional growth in the number of credit card transactions, especially for online purchases, has recently led to a substantial rise in fraudulent activities. Credit card security is a major concern for any business establishment. With that in mind, it is hard to identify the credit card fraud. Implementation of efficient fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses.

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Enhanced credit card fraud detection based on attention mechanism and LSTM deep model

Journal Of Big Data ◽

10.1186/s40537-021-00541-8 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Ibtissam Benchaji ◽

Samira Douzi ◽

Bouabid El Ouahidi ◽

Jaafar Jaafari

Keyword(s):

Credit Card ◽

Short Term Memory ◽

Fraud Detection ◽

Input Sequence ◽

Attention Mechanism ◽

Credit Card Fraud ◽

Detection Systems ◽

Deep Model ◽

Efficiency And Effectiveness ◽

Card Payment

AbstractAs credit card becomes the most popular payment mode particularly in the online sector, the fraudulent activities using credit card payment technologies are rapidly increasing as a result. For this end, it is obligatory for financial institutions to continuously improve their fraud detection systems to reduce huge losses. The purpose of this paper is to develop a novel system for credit card fraud detection based on sequential modeling of data, using attention mechanism and LSTM deep recurrent neural networks. The proposed model, compared to previous studies, considers the sequential nature of transactional data and allows the classifier to identify the most important transactions in the input sequence that predict at higher accuracy fraudulent transactions. Precisely, the robustness of our model is built by combining the strength of three sub-methods; the uniform manifold approximation and projection (UMAP) for selecting the most useful predictive features, the Long Short Term Memory (LSTM) networks for incorporating transaction sequences and the attention mechanism to enhance LSTM performances. The experimentations of our model give strong results in terms of efficiency and effectiveness.

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Credit Card Fraud Detection using Machine Learning: A Systematic Literature Review

ABC Journal of Advanced Research ◽

10.18034/abcjar.v6i2.547 ◽

2017 ◽

Vol 6 (2) ◽

pp. 113-120

Author(s):

Harish Paruchuri

Keyword(s):

Machine Learning ◽

Literature Review ◽

Systematic Literature Review ◽

Credit Card ◽

Fraud Detection ◽

Credit Card Fraud ◽

Online Purchases ◽

Online Mode

Companies want to give more and more facilities to their customers. One of these facilities is the online mode of buying goods. The customers now can buy the required goods online but this is also an opportunity for criminals to do frauds. The criminals can theft the information of any cardholder and use it for online purchases until the cardholder contacts the bank to block the card. This paper shows the different algorithms of machine learning that are used for detecting this kind of transaction.

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