A Novel Framework for Credit Card Fraud Detection

Mniai, Ayoub; Tarik, Mouna; Jebari, Khalid

doi:10.1109/access.2023.3323842

Cited by 9 publications

(2 citation statements)

References 45 publications

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“…The proposed framework improves detection performance and reduces computational requirements. Work in [24] presents the FFD framework for fraud detection, incorporating undersampling, feature selection, and SVDD. A modified PSO algorithm enhances hyperparameter optimization, resulting in effective fraud detection.…”

Section: Detailed Study Of Fraud Detection Methodsmentioning

confidence: 99%

Machine Learning Models for Fraud Detection: A Comprehensive Review and Empirical Analysis

L. K. Vishwamitra

2024

jes

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An in-depth familiarity with ML and DL models for fraud detection is essential due to the growing frequency and complexity of fraudulent activity across many domains. Despite the abundance of research on the subject, empirical analyses of these models, especially in their real-time implementations, are typically lacking. This study fills that need by meticulously reviewing and analysing ML and DL models developed for fraud detection. We draw attention to the shortcomings of existing approaches, which are crucial in the ever-changing field of fraud detection and include problems with recall, scalability, complexity, precision, and accuracy. By evaluating various ML and DL models using these measures, our evaluation method is based on a rigorous empirical approach. Provided that insights into the practical consequences as well as flexibility of each model in real-time circumstances, they thoroughly assess their performance. There are many ways in which this work will be useful; for example, it will help professionals choose the best models for their fraud detection needs, improve academic knowledge of these models in practice, and open the door to more studies that will concentrate on developing better fraud detection tools. This comprehensive research gives academics and companies a foundation for better, more effective and more scalable fraud detection systems in this period of essential digital security.

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Section: Detailed Study Of Fraud Detection Methodsmentioning

confidence: 99%

Machine Learning Models for Fraud Detection: A Comprehensive Review and Empirical Analysis

L. K. Vishwamitra

2024

jes

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“…In the SVDD model updating process, the influence between samples is continuous, and this influence is not only temporal but also spatial, where neighboring samples will inevitably affect the sample at that point in space. Therefore, based on the time decay weight, the idea of K-nearest neighbors (KNN) can be borrowed [24], proposing the time density weight function for each the sample x i :…”

Section: In-depth Screening Mechanism Based On Time Densitymentioning

confidence: 99%

Research on online anomaly detection methods for bearing degradation

Jin,

Xu,

et al. 2024

Meas. Sci. Technol.

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In industrial applications, rolling bearings operate under conditions of high precision and high speed, and their physical and mechanical characteristics change with the increase in operating time. Traditional diagnostic methods struggle to adapt well to the changing characteristics of bearings for online anomaly detection. Therefore, this research proposes an online anomaly detection method for rolling bearings based on Time-density-weighted Incremental SupportVector Data Description (TISVDD). A classification strategy is proposed to prevent sample misclassification in the updating process. The Detection Boundary is established based onSVDD decision boundary to enhance the recognition of abnormal samples in the process of model updating. A dual-screening mechanism update strategy for support vectors is proposed.It involves establishing a preliminary screening mechanism based on the Elimination Boundary.On this basis, an in-depth screening mechanism based on time-density weight is introduced by considering spatiotemporal characteristics of samples, enhancing the real-time performance of online anomaly detection for bearings. Building upon the fused dual-boundary SVDD, a Time-density-weighted Incremental SVDD (TISVDD) framework for online anomaly detection is proposed, enabling the detection model to dynamically update in response to data changes over time. To validate the effectiveness of the proposed method, experi-ments were conducted using the XJTU-SY bearing dataset and real-time datasets collected on an online hardware platform.The results demonstrate the effectiveness and superiority of the method in practical applications.

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Detecting Frauds and Payment Defaults on Credit Card Data Inherited With Imbalanced Class Distribution and Overlapping Class Problems: A Systematic Review

Kalid,

Khor,

et al. 2024

IEEE Access

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Credit card payments are one popular e-payment option apart from cash payments. Recent reports show that credit card fraud and payment defaults are increasing annually and are alarming. Thus, researchers have attempted various machine learning techniques to address these two challenges. However, they are challenged to mitigate the two major problems inherited in credit card data: (i) imbalanced class distribution and (ii) overlapping classes. Mitigating these problems shall effectively detect credit card frauds and payment defaults, thus benefiting card issuers and holders. Hence, this paper aims to develop a systematic review using PRISMA to identify and compare various credit card datasets, machine learning techniques, and evaluation metrics. Subsequently, we provide recommendations for handling these two problems. We extracted research papers from 2016 to 2023 from ScienceDirect, Springer, Association and Computing Machinery (ACM), and IEEE databases. The papers shall be included if written in English and published in peer-reviewed and indexed journals or conference proceedings. Finally, 87 papers were selected based on the eligibility criteria. Based on our findings, the European and Taiwan datasets are widely used in the research community. However, most researchers focus on tackling imbalanced class distribution rather than two problems together. We recommended to the research community the application of deep learning, ensemble learning, and sampling methods to effectively detect fraud and payment defaults on credit card datasets that inherit the two problems. In evaluating the machine learning algorithms, we recommend using metrics that can separately evaluate the algorithms' performance in detecting frauds/payment defaults and normal transactions.INDEX TERMS PRISMA, credit card fraud, payment default, imbalanced class distribution, overlapping classes.

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A Novel Framework for Credit Card Fraud Detection

Cited by 9 publications

References 45 publications

Machine Learning Models for Fraud Detection: A Comprehensive Review and Empirical Analysis

Machine Learning Models for Fraud Detection: A Comprehensive Review and Empirical Analysis

Research on online anomaly detection methods for bearing degradation

Detecting Frauds and Payment Defaults on Credit Card Data Inherited With Imbalanced Class Distribution and Overlapping Class Problems: A Systematic Review

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