Credit Card Fraud Detection Using Machine Learning

Tanouz, D.; Subramanian, R. Raja; Eswar, D.; Reddy, Guntakandla Vikranth; Kumar, Ajay; Praneeth, CH V N M

doi:10.1109/iciccs51141.2021.9432308

Cited by 61 publications

(29 citation statements)

References 5 publications

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“…It is yet to be discovered whether it will be applicable to people of the age group below 18 yrs.) [30,31].…”

Section: Discussionmentioning

confidence: 99%

An Optimized Approach Using Transfer Learning to Detect Drunk Driving

Kumar¹,

Kumar²,

Singh³

et al. 2022

Scientific Programming

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Although the statistics show a slow decline in traffic accidents in many countries over the last few years, drunk or drug-influenced driving still contributes to enough shares in those records to act. Nowadays, breath analysers are used to estimate breath alcohol content (BAC) by law enforcement as a preliminary alcohol screening in many countries. Therefore, since breath analysers or field sobriety testers do not accurately measure BAC, the analysis of blood samples of individuals is required for further action. Many researchers have presented various approaches to detect drunk driving, for example, using sensors, face recognition, and a driver’s behaviour to confound the shortcomings of the time-honoured approach using breath analysers. But each one has some limitations. This study proposed a plan to distinguish between drivers’ states, that is, sober or drunk, by the use of transfer learning from the convolutional neural network (CNN) features to the random forest (RF) features with an accuracy of up to 93%, which is higher than that of existing models. With the same dataset, to validate our research, a comparative analysis was performed with other existing model classifiers such as the simple vector machine (SVM) with an accuracy of 65% and the K-nearest neighbour (KNN) with an accuracy of 62%, and it was found that our approach is an optimized approach in terms of accuracy, precision, recall, F1-score, AUC-ROC curve, and Matthew’s correlation coefficient (MCC) with confusion matrix.

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“…It is yet to be discovered whether it will be applicable to people of the age group below 18 yrs.) [30,31].…”

Section: Discussionmentioning

confidence: 99%

An Optimized Approach Using Transfer Learning to Detect Drunk Driving

Kumar¹,

Kumar²,

Singh³

et al. 2022

Scientific Programming

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“…The structure for machine learning model building for credit card fraud detection is available in the work of Tanouz et al [60]. The European cardholders dataset was utilized for the determination of the chosen methods' performance.…”

Section: Literature Review and Backgroundmentioning

confidence: 99%

The XGBoost Approach Tuned by TLB Metaheuristics for Fraud Detection

Petrovic¹,

Antonijevic²,

Strumberger³

et al. 2023

Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)

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The recent pandemic had a major impact on online transactions. With this trend, credit card fraud increased. For the solution to this problem the authors explore existing solutions and propose an optimized solution. The solution is based on an extreme gradient boosting algorithm (XGBoost) and a teaching-learning-based-optimization algorithm. The dataset optimizes the hyperparameters of the XGBoost which is utilized as the main driver for the solution. The evaluation was performed among other similar techniques that have solved this problem successfully in the past. Standard performance metrics were applied which are accuracy, recall, precision, Matthews correlation coefficient, and area under the curve. The result of this research presents a dominant solution that was proposed and successfully outperformed all other compared solutions overall.

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“…• Logistic Regression [8, 10-12, 22, 28, 29] • Support Vector Machines [21,22,27,29,30] • Neural Networks [1,3,5,13,18,26,27,31] • Decision Trees [6,7,11,18,22,28] • Random Forests [6-8, 11, 12, 15, 21, 22, 28] • Naive Bayes [11,[27][28][29] • K-Nearest Neighbors [11,22,29] • Isolation Forest [13,22,23] • Local Outlier Factor [10,13,23] Random Forests and Neural Networks generally produced good results, however, some researchers reported that Neural Networks took a long time to train. In general, the best suited algorithm depends on the properties of the data at hand, hence, the reason for many researchers taking the route of comparing the algorithms to determine the one that was most appropriate.…”

Section: Related Workmentioning

confidence: 99%

Framework for Credit Card Fraud Detection Using Benefit-Based Learning and Periodic Features

Sooklal

Hosein

2023

Preprint

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Online credit card fraud is an ongoing problem and with the recent COVID-19 pandemic, there has been a surge of merchants moving their businesses online. It is therefore crucial to identify fraudulent activities before it causes loss to both the bank and its customers. Due to the dynamic nature of fraudsters as well as customer spending behavior, machine learning algorithms are appropriate for this task. However, credit card fraud data is typically imbalanced, favoring the positive class (legitimate transactions), causing traditional machine learning algorithms to err on the side of this majority class; since they consider equal costs and benefits for different decision outcomes when training. Nevertheless, it is more beneficial to correctly identify fraudulent transactions. Therefore, in this paper, we propose a technique for identifying credit card fraud that first accounts for customer spending patterns by aggregating transactions to creative new features based on periodic data. Then, we consider benefits and costs when training an XGBoost classifier in order to achieve maximum benefits. We also evaluate the performance of the classifier using benefits and costs. We demonstrate the effectiveness of our approach using data provided by a bank.

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Credit Card Fraud Detection Using Machine Learning

Cited by 61 publications

References 5 publications

An Optimized Approach Using Transfer Learning to Detect Drunk Driving

An Optimized Approach Using Transfer Learning to Detect Drunk Driving

The XGBoost Approach Tuned by TLB Metaheuristics for Fraud Detection

Framework for Credit Card Fraud Detection Using Benefit-Based Learning and Periodic Features

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