A Survey on Credit Card Fraud Detection Techniques in Banking Industry for Cyber Security

Btoush, Eyad; Zhou, Xujuan; Gururaian, Rai; Chan, Kenny L.; Tao, Xiaohui

doi:10.1109/besc53957.2021.9635559

Cited by 14 publications

(5 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since in Ukraine, the influence of private entities on the security of the national economy is minimal, this notion may be worth considering (Yesimov & Borovikova, 2023). Methods of detecting credit card fraud in the banking industry were studied by E. Btoush et al (2021). They formulated a thesis, which was confirmed by the author's observations on the growth and diversity of criminal technologies aimed at deceiving consumers of banking services, assessing various techniques, and identifying their advantages and disadvantages.…”

Section: Discussionmentioning

confidence: 99%

Untitled

2024

devt

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

Untitled

2024

devt

View full text Add to dashboard Cite

show abstract

“…K-means depends on the distance in splitting the dataset into clusters, which is useful when we want to predict the healthy people "not have diabetes" in the second dataset based on the first dataset. Also, it works well when we use PCA because it focuses on some features; also, merging with PCA will take more time, but the k-means is comparatively simple to implement and Easily adjusts to new instances [24] B. Principal Components Analysis (PCA)…”

Section: A K-meansmentioning

confidence: 99%

Hybrid K-means and Principal Component Analysis (PCA) for Diabetes Prediction

Abed Mohammed,

Sumari,

Attabi

2024

IJCDS

View full text Add to dashboard Cite

Diabetes is the "silent killer," stealing the lives of millions of people worldwide. There are many reasons for diabetes, such as increasing glucose, Cholesterol, systolic BP, and Age. These are considered to be the four primary causes of diabetes. The challenge in diabetes is predicting the human illness early to start treatment immediately after discovering diabetes; this can be the most challenging thing in diabetes discovery because tens of features may cause diabetes. This study proposes a model consisting of data mining and Machine Learning (ML) algorithms to predict if humans can have diabetes or not in the future. The prediction is made up of compensating two datasets; one dataset is used to reconfirm the other dataset in order to make a more accurate prediction. This can be performed using the k-means-PCA hybrid model and the highest weight selection of features that widely cause diabetes. The selected features help the ML algorithm predict the model's accuracy, which indicates the prediction model's accuracy. Simulation results show that the number of predict-diabetic patients increased from 53 from the original datasets to 142 after applying the proposed model. Simulation outcomes also prove that the Random Forest ML model gives the highest accuracy of other ML models, reaching 95.2%.

show abstract

“…Thus, leaving derogatory imprints of unquantifiable economic losses, customer frictions, reputation and infrastructure damages; that perhaps instills fears, psychological defects on victims (e.g. Cardholders, Merchant, and Card issuers); as it triggers national security threats and vulnerabilities [1][2][3][4]. To which cognizance is emphasize for defensive measures in ensuring regularities [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Deep convolutional neural network based synthetic minority over sampling technique: a forfending model for fraudulent credit card transactions in financial institution

Salaudeen,

GABI,

Garba

et al. 2024

J. Nig. Soc. Phys. Sci.

View full text Add to dashboard Cite

Fraudulent credit card transactions are committed by unauthorized individuals and organizations employing methods such as phishing and social engineering fraud tactics. Researchers propose several Machine Learning (ML) techniques to deter the challenges of credit card fraud. However, the ML approaches are endorsed with some challenges, which makes the detection of credit card fraud extremely difficult. This study proposes a Deep Convolutional Neural Network (DCNN) with Synthetic Minority Oversampling Techniques (SMOTE) as an ideal solution. Kaggle datasets with 284,807 records and 31 features were exploited. Implementation was performed on the Google Colab cloud-based platform, embedding a Jupyter notebook setting with Graphical Processing Units (GPUs). Two experiments were conducted; the first was probed to determine suitable models among baseline models: Logistic Regression (LR), Random Forest (RF), Isolation Forest, and a single Deep Learning (DL) model of Multiple Layer Perceptron (MLP). The baseline models yielded an overfitting accuracy score, with recall, specificity, precision, and F1-score all presenting 1.00% respectively. This outcome is not sufficient in establishing findings on imbalanced data distribution as it's biased. This led to the construction of a new ML model incorporating Light Gradient Boosting Machine (LGBM), with Artificial Neural Network (ANN) and the proposed DCNN+SMOTE for the second experimental phase alongside baseline models. Experimental results via simulation show the proposed DCNN+SMOTE yielded awesome superclass performance across the board, displaying 1.00% results respectively. Its Error Rate (ER) and Null Error Rate (NER) are 0.00% distinctly. Meanwhile, the False Positive Rate (FPR) yields a 0.001% result, lesser and better than the baseline models.

show abstract

A Survey on Credit Card Fraud Detection Techniques in Banking Industry for Cyber Security

Cited by 14 publications

References 22 publications

Untitled

Untitled

Hybrid K-means and Principal Component Analysis (PCA) for Diabetes Prediction

Deep convolutional neural network based synthetic minority over sampling technique: a forfending model for fraudulent credit card transactions in financial institution

Contact Info

Product

Resources

About