Analysis of Various Credit Card Fraud Detection Techniques

Preeti, S.; Ashima, .

doi:10.26438/ijcse/v7i6.12121216

Cited by 5 publications

(3 citation statements)

References 0 publications

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“…[6] Real time dataset used was collected from kaggle.com. The dataset consists of 31 attributes and 2848 rows [7].After collection of the dataset and data about data was uploaded and preprocessed with respect to data scenario. All null values and missing values have been classified into two segments namely cleaning and cleansing the unstructured data.…”

Section: Methodsmentioning

confidence: 99%

A Fraudulent Transaction Prediction in Credit Card by Using Novel LGBA over LR Algorithms

2022

Advances in Parallel Computing Algorithms, Tools and Paradigms

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The Research is to predict the accuracy of credit card fraudulent transactions using the Light Gradient Booster algorithm.Novel Light Gradient Booster with sample size =10 then Logistic Regression of sample size = 10 were executed for estimating accuracy rate of credit card fraudulent transactions. The sigmoid functions used in Light Gradient maps the values between 0 and 1. Light Gradient Booster Algorithm has a Maximum accuracy (91.6%) while comparing to the performance of Logistic Regression (81.4%). There was a statistical significance difference between Light Gradient Booster and Logistic Regression with p = 0.0001 (p<0.05) based on 2-tailed analysis. Light Gradient algorithm helps in predicting with better accuracy percentage of credit card fraudulent transaction than Logistic regression.

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Section: Methodsmentioning

confidence: 99%

A Fraudulent Transaction Prediction in Credit Card by Using Novel LGBA over LR Algorithms

2022

Advances in Parallel Computing Algorithms, Tools and Paradigms

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“…In this section, exploration is delved on recent related studies. To which predated proposed systems and techniques for credit card fraud detection in financial institution is presented [12,13,25,33]. Besides, fraud prevention and detection mechanism with ethics is germane for consideration before delving to address any fraudulent scenarios affecting financial institutions.…”

Section: Related Workmentioning

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.

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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.

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“…These three models were trained using the encoded data. SVM, KNN and LR were chosen as they are the popular machine learning methods used by other researchers for credit card fraud detection [15][16][17].…”

Section: Dimensionality Reductionmentioning

confidence: 99%

Autoencoders with Reconstruction Error and Dimensionality Reduction for Credit Card Fraud Detection

Rosley¹,

Tong²,

Ng³

et al. 2022

Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)

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The increase in credit card transactions has inevitably caused an increase in credit card fraud. A total of 157,688 fraud cases occurred in 2018 worldwide, causing a total loss of $24.26 billion. This paper proposes using two types of autoencoder models to detect credit card fraud. The first type uses reconstruction error to detect anomalies in the data. The model detects fraud by defining a threshold in the reconstruction error to flag the transactions as legitimate or fraud. The second type performs dimensionality reduction to encode the data and removes noises. The encoded data were then used to train three other models: K-nearest neighbours (KNN), logistic regression (LR), and support vector machine (SVM). We then applied these models to a European bank's imbalanced credit card data set. A comparison was made between the two autoencoder types and three baseline models: KNN, LR and SVM. The results showed that both autoencoders gave a good and comparable performance in detecting credit card frauds.

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Analysis of Various Credit Card Fraud Detection Techniques

Cited by 5 publications

References 0 publications

A Fraudulent Transaction Prediction in Credit Card by Using Novel LGBA over LR Algorithms

A Fraudulent Transaction Prediction in Credit Card by Using Novel LGBA over LR Algorithms

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

Autoencoders with Reconstruction Error and Dimensionality Reduction for Credit Card Fraud Detection

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