A systematic review of literature on credit card cyber fraud detection using machine and deep learning

Btoush, Eyad; Zhou, Xujuan; Gururajan, Raj; Chan, Ka C.; Genrich, Rohan; Sankaran, Prema

doi:10.7717/peerj-cs.1278

Cited by 14 publications

(4 citation statements)

References 131 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Selected Algorithms Efficient Algorithm Accuracy Kibria and Sevkli [17] LR, Deep Learning, and SVM Deep Learning 87.10% Naveen and Diwan [30] LR, QDA and SVM LR 99.38% Shaji et al [26] ANN and SVM, Both 88.00% Sinayobye et al [31] KNN, DT, RF, SVM, LR KNN 82.60% Btoush et al [32] Deep Learning DL 95.76% Taha et al [33] Optimized Light Gradient Boosting Machine OLGBM 98.40%…”

Section: Authorsmentioning

confidence: 99%

UNMASKING FRAUDSTERS: Ensemble Features Selection to Enhance Random Forest Fraud Detection

Akazue,

Debekeme,

Edje

et al. 2023

J. Comput. Theor. Appl.

View full text Add to dashboard Cite

Fraud detection is used in various industries, including banking institutes, finance, insurance, government agencies, etc. Recent increases in the number of fraud attempts make fraud detection crucial for safeguarding financial information that is confidential or personal. Many types of fraud problems exist, including card-not-present fraud, fake Marchant, counterfeit checks, stolen credit cards, and others. An ensemble feature selection technique based on Recursive feature elimination (RFE), Information gain (IG), and Chi-Squared (X2) in concurrence with the Random Forest algorithm, was proposed to give research findings and results on fraud detection and prevention. The objective was to choose the essential features for training the model. The Receiver Operating Characteristic (ROC) Score, Accuracy, F1 Score, and Precision are used to evaluate the model's performance. The findings show that the model can differentiate between fraudulent transactions and those that are not, with an ROC Score of 95.83% and an Accuracy of 99.6%. The F1 Score of 99.6%% and precision of 100% further sustain the model's ability to detect fraudulent transactions with the least false positives correctly. The ensemble feature selection technique reduced training time and did not compromise the model's performance, making it a valuable tool for businesses in preventing fraudulent transactions.

show abstract

Section: Authorsmentioning

confidence: 99%

UNMASKING FRAUDSTERS: Ensemble Features Selection to Enhance Random Forest Fraud Detection

Akazue,

Debekeme,

Edje

et al. 2023

J. Comput. Theor. Appl.

View full text Add to dashboard Cite

show abstract

“…To address the issue of class imbalance, the authors of [5] coupled SMOTE with under-sampling. A fuzzy multi-class SVM technique for unbalanced data was created by [6]. The authors of [11] suggested many methods to improve the classification performance of RF and LR while working with unbalanced data sets.…”

Section: Literature Reviewmentioning

confidence: 99%

“…When dealing with datasets that have a significant skew, a large number of algorithms find it difficult to distinguish between objects from minority classes. Systems designed to detect cyber fraud must act quicker to be effective [6]. The impact of new attack techniques on the conditional distribution of the data across the period is another significant area of worry.…”

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Networks-Based Novel Approach for Fraud Detection for the European Cardholders 2013 Dataset

Almarshad,

Gashgari,

Alzahrani

2023

IEEE Access

View full text Add to dashboard Cite

Credit card use poses a significant security issue on a global scale, with rule-based algorithms and traditional anomaly detection being two of the most often used methods. However, they are resourceintensive, time-consuming, and erroneous. Given fewer instances than legal payments, the dataset imbalance has become a serious issue. On the other hand, the generative technique is considered an effective way to rebalance the imbalanced class issue, as this technique balances both minority and majority classes before the training. In a more recent period, GAN is considered one of the most popular data generative techniques, as it is used in significant data settings. Hence, the research under study explores a classification system to detect fraudulent credit card transactions that are being trained using the European Cardholders 2013 dataset. It has 30 features, 28 of which are hidden due to sensitive information. Fraud activity accounts for less than 1% of the entire transaction volume of $284807. Additionally, GANs is a generative model based on game theory, in which a generator G and a discriminator D compete with one another. The generator's goal is to make the discriminator uncertain. Distinguishing between instances from the generator and those from the original dataset is the discriminator's goal, and we can increase classifiers' discriminating strength by training GANs on a set of fraudulent credit card transactions. According to the outcome, our model outperformed the earlier experiments with an AUC score of 0.999. Additionally, it creates artificial data using GANs, enabling the production of a sizable volume of high-quality data. In terms of innovation and performance, this technique substantially improves over earlier research.

show abstract

“…Machine learning (ML) models learns these patterns via features of interest, which helps them identify these patterns as signature classification that deviates from the norm [40]. A variety of ML have yielded resultant success with its adoption in collaborative filtering algorithm to include: Logistic Regression [41]- [43], Deep Learning [44]- [46], Bayesian model [47]- [49], Support Vector Machine [50]- [52], Random Forest [53]- [55], K-Nearest Neighbors [56]- [58], and in other models [59]- [61]. Their flexibility and performance is greatly hampered/degraded with the adopted choice in feature selection technique and data-preprocessing scheme [62], [63].…”

mentioning

confidence: 99%

StreamBoostE: A Hybrid Boosting-Collaborative Filter Scheme for Adaptive User-Item Recommender for Streaming Services

Atuduhor, R.R.,

Okpor, M.D,

3Yoro, R.E.

et al. 2024

AIMS Research Journal

View full text Add to dashboard Cite

With home entertainment, selecting the perfect movie is a pervasive challenge, amplified by many streaming platforms like Netflix and Amazon Prime. This study advances a movie recommender system with collaborative filtering approach as implemented in Python titled StreamBoostE. We used the user-based and item-based similarity schemes on feature embedding to aid faster model construction and training for the tree-based gradient boosting ensemble. Employing both user- and item-based collaborative filtering with cosine similarity to ease feature embedding, the system assesses movies inter-relations via personalized user interest and preferences as submitted user titles with a focus on movie genre classification. Results shows the ensemble yields a recommender prediction accuracy of 0.9984 with F1 of 0.996. The major contribution of StreamBoostE is in its capability to expedite the movie selection process when integrated using flask API and streamlit for cross-channel integration in web-based platforms. It presents users with a list of top-10 recommended movies by genre similarity. The XGBoost ensemble performed best with the user-/item-based collaborative filtering scheme fused with feature embedding approach as a sampling method. Keywords: Random Forest, SMOTE, credit card fraud detection, feature selection, imbalanced dataset Aims Research Journal Reference Format: Atuduhor, R.R., Okpor, M.D., Yoro, R.E., Odiakaose, C.C., Emordi, F.U., Ojugo, A.A., Ako, R.E., Geteloma, V.O., Ejeh, P.O., Abere, R.A., Ifioko, A.M., & Brizimor, S.E. (2024): StreamBoostE: A Hybrid Boosting-Collaborative Filter Scheme for Adaptive User-Item Recommender for Streaming Services. Advances in Multidisciplinary and Scientific Research Journal Vol. 10. No. 2. Pp 89-106. www.isteams.net/aimsjournal. dx.doi.org/10.22624/AIMS/V10N2P8

show abstract

A systematic review of literature on credit card cyber fraud detection using machine and deep learning

Cited by 14 publications

References 131 publications

UNMASKING FRAUDSTERS: Ensemble Features Selection to Enhance Random Forest Fraud Detection

UNMASKING FRAUDSTERS: Ensemble Features Selection to Enhance Random Forest Fraud Detection

Generative Adversarial Networks-Based Novel Approach for Fraud Detection for the European Cardholders 2013 Dataset

StreamBoostE: A Hybrid Boosting-Collaborative Filter Scheme for Adaptive User-Item Recommender for Streaming Services

Contact Info

Product

Resources

About