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

Abstract

As 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.

An Innovative Sensing Machine Learning Technique to Detect Credit Card Frauds in Wireless Communications

“…A few of them are autoencoder-based detection [17], K-means deep network [18], and LSTM using attention mechanism dependence concerns are better addressed by RNN architecture. Ability to learn order dependency in sequence prediction problems as a behaviour required in complicated issue areas like as machine translation and speech recognition, among others [19]. But when dealing greater than 2D, noisy input data, the support vector machine's classification performance is significantly lower than when dealing with less than 2D, clean data.…”

Two-stage credit scoring using Bayesian approach

“…Additionally, neural networks have been applied for credit card fraud detection and several marketing tasks [29,30]. To consider the time-series characteristics of input features, the long short-term memory algorithm has been used to develop both credit card delinquency prediction and fraud detection models [8,9]. In addition, various machine learning algorithms, such as decision tree, support vector machine, random forest, and genetic algorithm, are widely used in fraud detection modeling [31][32][33].…”

“…However, although machine learning-based models are currently attracting attention as credit scoring techniques for significantly improving model performance, their interpretability regarding the credit evaluation results has a few limitations because they are black box models in which there is difficulty determining the relationship between the explanatory variable and dependent variable [4]. To overcome this limitation, studies on improving the interpretability of machine learning-based credit rating models have emerged [5][6][7][8][9].…”

Advancement of management information system for discovering fraud in master card based intelligent supervised machine learning and deep learning during SARS-CoV2

“…Picture captioning and Machine translation have both benefited greatly from its use. The mechanism of attention works by taking a weighted mean of the series of vectors is used to construct the context vector (includes the most significant info), which is then employed as input in the next layer ( Benchaji et al, 2021 ).…”