Credit Card Fraud Detection in Card-Not-Present Transactions: Where to Invest?

Mekterović, Igor; Karan, Mladen; Pintar, Damir; Brkić, Ljiljana

doi:10.3390/app11156766

Cited by 22 publications

(10 citation statements)

References 56 publications

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“…Results highlighted the superiority of newly developed models, notably the random forest, which consistently outperformed existing ones across multiple metrics. [11], explained various machine learning classifiers including Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Artificial Neural Network (ANN), and Naive Bayes (NB) were utilized for fraud detection. The study incorporated the Genetic Algorithm (GA) for feature selection.…”

Section: Literature Reviewmentioning

confidence: 99%

Credit Card Fraud Detection Using a Hybrid Machine Learning Algorithm

Singh,

Nimje,

Fulpatile

et al. 2024

Preprint

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The use of credit cards and online banking is expanding exponentially. As more individuals use debit cards, credit cards, and internet banking, the likelihood of falling victim to various forms of fraud also rises. Due to ignorance, credit card company customers have provided their card details, personal information, and one-time password to an unknown bogus caller on several occasions in the recent past. One of the biggest issues facing financial institutions and customers alike is credit card theft. It may result in large financial losses and harm to the financial institution's image. While there are several ways to identify credit card fraud, one of the best is machine learning.By using past data, machine learning algorithms may be trained to recognize trends and abnormalities that point to fraud. The state-of-the-art in machine learning for credit card fraud detection is reviewed in this study. Numerous research on the same subject have been conducted in the past with a variety of well-known machine-learning techniques. However, with a grasp of the notion of hybrid machine learning, we will now examine how well hybrid machine learning performs on the same problem statement.

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Section: Literature Reviewmentioning

confidence: 99%

Credit Card Fraud Detection Using a Hybrid Machine Learning Algorithm

Singh,

Nimje,

Fulpatile

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Most credit card frauds involve card not present fraud. When a fraudster has access to genuine information, CNP fraud protection is difficult [75].…”

Section: Major Types Of Credit Card Fraud Typesmentioning

confidence: 99%

Credit Card Fraud Detection using Machine Learning and Data Mining Techniques - a Literature Survey

2023

IJAEML

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Purpose: To understand the algorithms used in Credit Card Fraud Detection (CCFD) using Machine Learning (ML) and Data Mining (DM) techniques, Review key findings in the area and come up with research gaps or unresolved problem. To become knowledgeable about the current discussions in the area of ML and DM. Design/Methodology/Approach: The survey on CCFD using ML and DM was conducted based on data from academic papers, web articles, conference proceedings, journals and other sources. Information is reviewed and analysed. Results/Findings: Identification of credit card fraud is essential for protecting a person's or an organization's assets. Even though we have various safeguards in place to prevent fraudulent activity, con artists may develop a method to get around the checkpoints. We must create straightforward and efficient algorithms employing ML and DM to anticipate fraudulent activities in advance. Originality/Value: Study of ML and DM algorithms in CCFD from diverse sources is done. This area needs study due to recent methods by fraudsters in digital crime have developed. The information acquired will be helpful for creating new methodologies or improving the outcomes of current algorithms. Type of Paper: Literature Review.

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“…Consequently, investigators frequently use neural networks to approximate the mean and modification of normal distribution. Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in DL models [24], [25]. The LSTM network is compatible with categorising, processing and building predictions based on time sequence data.…”

Section: B Deep Learning Approachesmentioning

confidence: 99%

Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms

et al. 2022

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People can make use of credit card for online transactions as it provides efficient and easy-to-use facility. With the increase in usage of credit cards, the capacity of credit card misuse has also enhanced. Credit card frauds cause significant financial losses for both credit card holders and financial companies. In this research study, the main aim is to detect such frauds which include the accessibility of public data, high-class imbalance data and fraud nature can be changed and the false alarm is in high rates. The relevant literature presents a number of machines learning based approaches for credit card detection.Such as Extreme Learning Method, Decision Tree, Random Forest, Support Vector Machine, Logistic Regression and XG Boost. But due to low accuracy, there is still need to apply state of the art deep learning algorithms to reduce the fraud losses.The main focus has been to apply the recent development of deep learning algorithms for this purpose. Comparative analysis of both machine learning and deep learning algorithms was performed to find efficient outcomes. The detail empirical analysis is carried out using European card benchmark dataset for fraud detection. Machine learning algorithm is first applied on the data set which showed improvement in the accuracy of detection of the frauds to some extent. Later, three architectures based on convolutional neural network are applied to improve fraud detection performance. Further addition of layers further increased the accuracy of detection. A comprehensive empirical analysis has been carried out by applying variations in number of hidden layers, epochs and applying the latest models. The evaluation of research work shows the improved results achieved such as accuracy, f1-score, precision and AUC Curves having optimized values 99.9%,85.71%,93%,98% respectively. The purposed model outperforms over state of art machine learning and deep learning algorithms for credit card detection problems.In addition, we have performed experiments by balancing the data and applying deep learning algorithms to minimize the false negative rate. The proposed approaches can be implemented effectively for the real-world detection of credit card frauds.

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Credit Card Fraud Detection in Card-Not-Present Transactions: Where to Invest?

Cited by 22 publications

References 56 publications

Credit Card Fraud Detection Using a Hybrid Machine Learning Algorithm

Credit Card Fraud Detection Using a Hybrid Machine Learning Algorithm

Credit Card Fraud Detection using Machine Learning and Data Mining Techniques - a Literature Survey

Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms

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