Detection of fraudulent credit card transactions: A comparative analysis of data sampling and classification techniques

Mahesh, Konduri Praveen; Afrouz, Shaik Ashar; Areeckal, Anu Shaju

doi:10.1088/1742-6596/2161/1/012072

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…Tomek links corresponding instances of the opposite class that are the nearest neighbors to each other to provide better class separation to the decision borders. The results were improved to 99% compared with 94% in RUS [27,30].…”

Section: Class Imbalance Challengementioning

confidence: 86%

Credit Card Fraud Detection Challenges and Solutions: A Review

Saad,

Nadher,

Hameed

2024

Iraqi Journal of Science

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Credit card fraud has become an increasing problem due to the growing reliance on electronic payment systems and technological advances that have improved fraud techniques. Numerous financial institutions are looking for the best ways to leverage technological advancements to provide better services to their end users, and researchers used various protection methods to provide security and privacy for credit cards. Therefore, it is necessary to identify the challenges and the proposed solutions to address them. This review provides an overview of the most recent research on the detection of fraudulent credit card transactions to protect those transactions from tampering or improper use, which includes imbalance classes, concept drift, and verification latency problems using machine learning and deep learning. It also provides valuable information for academic and industrial researchers and opens new avenues for research aimed at developing robust fraud detection systems.

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Section: Class Imbalance Challengementioning

confidence: 86%

Credit Card Fraud Detection Challenges and Solutions: A Review

Saad,

Nadher,

Hameed

2024

Iraqi Journal of Science

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“…The authors noticed that oversampling techniques improved the performance of the models and claimed that there was no preference for one oversampling technique over another, as everything depended on the type of machine learning (ML) algorithm being used. Other researchers [5] explored different undersampling techniques using SMOTE and SMOTE-Tomek for unbalanced data. The classification models used in this study (KNN, LR, RF, and SVM) were trained on balanced data to detect fraudulent credit card transactions.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Undersampling and Oversampling for Handling Imbalanced Credit Card Data

Alamri,

Ykhlef

2024

IEEE Access

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Recent developments in the use of credit cards for a range of daily life activities have increased credit card fraud and caused huge financial losses for individuals and financial institutions. Most credit card frauds are conducted online through illegal payment authorizations by data breaches, phishing, or scams. Many solutions have been suggested for this issue, but they all face the major challenge of building an effective detection model using highly imbalanced class data. Most sampling techniques used for class imbalance have limitations, such as overlapping and overfitting, which cause inaccurate learning and are slowed down by noisy features. Herein, a hybrid Tomek links BIRCH clustering borderline SMOTE (BCB-SMOTE) sampling method is proposed to balance a highly skewed credit card transaction dataset. First, Tomek links were used to undersample majority instances and remove noise, and then BIRCH clustering was applied to cluster the data and oversample minority instances using B-SMOTE. The credit card frauddetection model was run using a random forest (RF) classifier. The proposed method achieved a higher F1score (85.20%) than the baseline sampling techniques tested for comparison. Because of the enormous number of credit card transactions, there was still a small false-positive rate. The proposed method improves the detection performance owing to the well-organized balancing of the dataset.

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“…The main data balancing methods include changing data distribution and improving the algorithm's level. The former involves over-sampling algorithms, such as SMOTE [5] and ADASYN [6], combined with under-sampling algorithms [7][8][9][10][11][12], which generate new samples.…”

Section: Introductionmentioning

confidence: 99%

Unbalanced credit fraud modeling based on bagging and bayesian optimization

Kashmoola,

Aziz,

Qays

et al. 2023

EEJET

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Credit fraud modeling is a crucial area of research that is highly relevant to the credit loan industry. Effective risk management is a key factor in providing quality credit services and directly impacts the profitability and bad debt ratio of leading organizations in this sector. However, when the distribution of credit fraud data is highly unbalanced, it can lead to noise errors caused by information distortion, periodic statistical errors, and model biases during training. This can cause unfair results for the minority class (target class) and increase the risk of overfitting. While traditional data balancing methods can reduce bias in models towards the majority class in relatively unbalanced data, they may not be effective in highly unbalanced scenarios. To address this challenge, this paper proposes using Bagging algorithms such as Random Forest and Bagging to model highly unbalanced credit fraud data. Bayesian optimization is utilized to find hyperparameters and determine the accuracy of the minority class as an optimization function for the model, which is tested with real European credit card fraud data. The results of the proposed packing algorithms are compared with traditional data balancing methods such as Balanced Bagging and Balanced Random Forest. The study found that traditional data balancing methods may not be compatible with excessively unbalanced data, whereas Bagging algorithms show promise as a solution for modeling such data. The proposed method for finding hyperparameters effectively deals with highly unbalanced data. It achieved precision, recall, and F1-score for the minority category of 0.94, 0.81, and 0.87, respectively. The study emphasizes the importance of addressing the challenges associated with unbalanced credit fraud data to improve the accuracy and fairness of credit fraud models

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Detection of fraudulent credit card transactions: A comparative analysis of data sampling and classification techniques

Cited by 7 publications

References 6 publications

Credit Card Fraud Detection Challenges and Solutions: A Review

Credit Card Fraud Detection Challenges and Solutions: A Review

Hybrid Undersampling and Oversampling for Handling Imbalanced Credit Card Data

Unbalanced credit fraud modeling based on bagging and bayesian optimization

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