Ensemble of deep sequential models for credit card fraud detection

Forough, Javad; Momtazi, Saeedeh

doi:10.1016/j.asoc.2020.106883

Cited by 105 publications

(48 citation statements)

References 20 publications

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“…The optimization problem is solved by NSGA-III because it not only enhances the diversity of the new population but also requires computing power with a small population size [ 41 , 42 ]. There are some previous works adopted hybrid algorithms such as GRU and LSTM for credit card fraud detection [ 43 ], RNN and LSTM for spoken language understanding [ 44 ], RNN and GRU for state-of-charge detection for lithium-ion battery [ 45 ], and RNN, GRU, and LSTM for rumor detection in social media [ 46 ]. These support the applicability and effectiveness of merging RNN, GRU, and LSTM algorithms which takes advantages from each of the algorithm.…”

Section: Methodology Of Proposed Nsga-iii Optimized Rnn-gru-lstm Modelmentioning

confidence: 99%

Extended-Range Prediction Model Using NSGA-III Optimized RNN-GRU-LSTM for Driver Stress and Drowsiness

Chui¹,

Gupta

Liu

et al. 2021

Sensors

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Road traffic accidents have been listed in the top 10 global causes of death for many decades. Traditional measures such as education and legislation have contributed to limited improvements in terms of reducing accidents due to people driving in undesirable statuses, such as when suffering from stress or drowsiness. Attention is drawn to predicting drivers’ future status so that precautions can be taken in advance as effective preventative measures. Common prediction algorithms include recurrent neural networks (RNNs), gated recurrent units (GRUs), and long short-term memory (LSTM) networks. To benefit from the advantages of each algorithm, nondominated sorting genetic algorithm-III (NSGA-III) can be applied to merge the three algorithms. This is named NSGA-III-optimized RNN-GRU-LSTM. An analysis can be made to compare the proposed prediction algorithm with the individual RNN, GRU, and LSTM algorithms. Our proposed model improves the overall accuracy by 11.2–13.6% and 10.2–12.2% in driver stress prediction and driver drowsiness prediction, respectively. Likewise, it improves the overall accuracy by 6.9–12.7% and 6.9–8.9%, respectively, compared with boosting learning with multiple RNNs, multiple GRUs, and multiple LSTMs algorithms. Compared with existing works, this proposal offers to enhance performance by taking some key factors into account—namely, using a real-world driving dataset, a greater sample size, hybrid algorithms, and cross-validation. Future research directions have been suggested for further exploration and performance enhancement.

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Section: Methodology Of Proposed Nsga-iii Optimized Rnn-gru-lstm Modelmentioning

confidence: 99%

Extended-Range Prediction Model Using NSGA-III Optimized RNN-GRU-LSTM for Driver Stress and Drowsiness

Chui¹,

Gupta

Liu

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…In another study [15], a sequential modeling-based ensemble model was developed by utilizing deep recurrent neural networks for the detection of fraudulent transactions. The authors utilized two real datasets to verify their model's performance.…”

Section: Related Workmentioning

confidence: 99%

A Novel text2IMG Mechanism of Credit Card Fraud Detection: A Deep Learning Approach

et al. 2022

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Online sales and purchases are increasing daily, and they generally involve credit card transactions. This not only provides convenience to the end-user but also increases the frequency of online credit card fraud. In the recent years, in some countries, this fraud increase has led to an exponential increase in credit card fraud detection, which has become increasingly important to address this security issue. Recent studies have proposed machine learning (ML)-based solutions for detecting fraudulent credit card transactions, but their detection scores still need improvement due to the imbalance of classes in any given dataset. Few approaches have achieved exceptional results on different datasets. In this study, the Kaggle dataset was used to develop a deep learning (DL)-based approach to solve the text data problem. A novel text2IMG conversion technique is proposed that generates small images. The images are fed into a CNN architecture with class weights using the inverse frequency method to resolve the class imbalance issue. DL and ML approaches were applied to verify the robustness and validity of the proposed system. An accuracy of 99.87% was achieved by Coarse-KNN using deep features of the proposed CNN.

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“…WELM with the dandelion algorithm yielded a high detection performance (Zhu et al, 2020 ). An ensemble model using sequential modeling of deep learning and voting mechanism of ANN was proposed in Forough and Momtazi ( 2021 ). Based on real-world credit card data, the time analysis results indicated the real time high efficiency of the proposed model as compared with other models (Forough & Momtazi, 2021 ).…”

Section: Literature Reviewmentioning

confidence: 99%

An intelligent payment card fraud detection system

et al. 2021

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Payment cards offer a simple and convenient method for making purchases. Owing to the increase in the usage of payment cards, especially in online purchases, fraud cases are on the rise. The rise creates financial risk and uncertainty, as in the commercial sector, it incurs billions of losses each year. However, real transaction records that can facilitate the development of effective predictive models for fraud detection are difficult to obtain, mainly because of issues related to confidentially of customer information. In this paper, we apply a total of 13 statistical and machine learning models for payment card fraud detection using both publicly available and real transaction records. The results from both original features and aggregated features are analyzed and compared. A statistical hypothesis test is conducted to evaluate whether the aggregated features identified by a genetic algorithm can offer a better discriminative power, as compared with the original features, in fraud detection. The outcomes positively ascertain the effectiveness of using aggregated features for undertaking real-world payment card fraud detection problems.

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Ensemble of deep sequential models for credit card fraud detection

Cited by 105 publications

References 20 publications

Extended-Range Prediction Model Using NSGA-III Optimized RNN-GRU-LSTM for Driver Stress and Drowsiness

Extended-Range Prediction Model Using NSGA-III Optimized RNN-GRU-LSTM for Driver Stress and Drowsiness

A Novel text2IMG Mechanism of Credit Card Fraud Detection: A Deep Learning Approach

An intelligent payment card fraud detection system

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