Credit Card-Not-Present Fraud Detection and Prevention Using Big Data Analytics Algorithms

Razaque, Abdul; Frej, Mohamed Ben Haj; Bektemyssova, Gulnara; Amsaad, Fathi; Almiani, Muder; Alotaibi, Aziz; Jhanjhi, N. Z.; Amanzholova, Saule; Alshammari, Majid

doi:10.3390/app13010057

Cited by 14 publications

(8 citation statements)

References 27 publications

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“…This raised more security concerns. Research [43] investigated the card-not-present form with non-contact fraud to deploy the card-notpresent detection/prevention heuristic. Another study [44] investigated a cardholders' capability to identify fraudulent transactions with Random Forest under-sampling to address data imbalance conflicts.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing the Random Forest Model via Synthetic Minority Oversampling Technique for Credit-Card Fraud Detection

Aghware,

Ojugo,

Adigwe

et al. 2024

J. Comput. Theor. Appl.

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Fraudsters increasingly exploit unauthorized credit card information for financial gain, targeting un-suspecting users, especially as financial institutions expand their services to semi-urban and rural areas. This, in turn, has continued to ripple across society, causing huge financial losses and lowering user trust implications for all cardholders. Thus, banks cum financial institutions are today poised to implement fraud detection schemes. Five algorithms were trained with and without the application of the Synthetic Minority Over-sampling Technique (SMOTE) to assess their performance. These algorithms included Random Forest (RF), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machines (SVM), and Logistic Regression (LR). The methodology was implemented and tested through an API using Flask and Streamlit in Python. Before applying SMOTE, the RF classifier outperformed the others with an accuracy of 0.9802, while the accuracies for LR, KNN, NB, and SVM were 0.9219, 0.9435, 0.9508, and 0.9008, respectively. Conversely, after the application of SMOTE, RF achieved a prediction accuracy of 0.9919, whereas LR, KNN, NB, and SVM attained accuracies of 0.9805, 0.9210, 0.9125, and 0.8145, respectively. These results highlight the effectiveness of combining RF with SMOTE to enhance prediction accuracy in credit card fraud detection.

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Section: Literature Reviewmentioning

confidence: 99%

Enhancing the Random Forest Model via Synthetic Minority Oversampling Technique for Credit-Card Fraud Detection

Aghware,

Ojugo,

Adigwe

et al. 2024

J. Comput. Theor. Appl.

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“…The cross-border issue of fraud is not discussed. A study [35] utilizes t-distributed stochastic neighbor embedding and principal component analysis for data reduction, while, LR for classi cation in credit cardnot-present fraud detection. Another form of fraud is new bank account fraud, the literature that employs machine learning techniques is lacking due to the possible lack of available data resources.…”

Section: Machine Learning Algorithms For Fraud Detectionmentioning

confidence: 99%

Modeling the Dynamic Behaviors of Bank Account Fraudsters Using Combined Simultaneous Game Theory with Neural Networks

USMAN,

Abdullahi,

Ran

et al. 2024

Preprint

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New bank account fraud is a significant problem causing financial losses in banking and finance. Existing statistical and machine-learning methods were used to detect fraud thereby preventing financial losses. However, most studies do not consider the dynamic behavior of fraudsters and often produce a high False Positive Rate (FPR). This study proposes the detection of new bank account fraud in the context of simultaneous game theory (SGT) with Neural Networks, the SGT involves two players, a fraudster, and bank officials attacking each other through Bayesian probability in a zero-sum. The influence of outliers within the SGT was tackled by adding a context feature for effective simulation of the dynamic behavior of fraudsters. The Neural Networks layer uses the simulated features for fraud context learning. The study is validated using Bank Account Fraud (BAF) Dataset on different machine-learning models. The Radial Basis Function Networks achieved FPR of 0.0% and 8.3% for fraud and non-fraud classes, respectively, while achieving True Positive Rate (TPR) of 91.7% and 100.0% for fraud and non-fraud classes, respectively. An improved Radial Basis Function Networks detect fraud by revealing fraudulent patterns and dynamic behaviors in higher dimensional data. The findings will enhance fraud detection and reduce customer attrition.

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“…The output gate controls the memory cell that receives the input signal (Benchaji et al, 2021). The gates in the LSTM network can learn sequential data as the gates control the flow of information (Razaque et al, 2023). Given the input , the previous hidden state , previous cell memory…”

Section: Long Short-term Memorymentioning

confidence: 99%

A combined framework based on LSTM autoencoder and XGBoost with adaptive threshold classification for credit card fraud detection

Prabha,

Priscilla

2024

The Scientific Temper

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The digital invasion of the banking and financial sectors made life simple and easy. Traditional machine learning models have been studied in credit card fraud detection, but these models are often difficult to find effective for unseen patterns. This study proposes a combined framework of deep learning and machine learning models. The long short term memory autoencoder (LSTMAE) with attention mechanism is developed to extract high-level features and avoid overfitting of the model. The extracted features serve as input to the powerful ensemble model XGBoost to classify legitimate and fraudulent transactions. As the focus of fraud detection is to increase the recall rate, an adaptive threshold technique is proposed to estimate an optimal threshold value to enhance performance. The experiment was done with the IEEE-CIS fraud detection dataset available in Kaggle. The proposed model with optimal threshold has an increase in predicting fraudulent transactions. The research findings are compared with conventional ensemble techniques to find the generalization of the model. The proposed LSTMAE-XGB w/ attention method attained a good precision and recall of 94.2 and 90.5%, respectively, at the optimal threshold of θ = 0.22. The experimental results proved that the proposed approach is better at finding fraudulent transactions than other cutting-edge models

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Credit Card-Not-Present Fraud Detection and Prevention Using Big Data Analytics Algorithms

Cited by 14 publications

References 27 publications

Enhancing the Random Forest Model via Synthetic Minority Oversampling Technique for Credit-Card Fraud Detection

Enhancing the Random Forest Model via Synthetic Minority Oversampling Technique for Credit-Card Fraud Detection

Modeling the Dynamic Behaviors of Bank Account Fraudsters Using Combined Simultaneous Game Theory with Neural Networks

A combined framework based on LSTM autoencoder and XGBoost with adaptive threshold classification for credit card fraud detection

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