Fraud Analysis Approaches in the Age of Big Data - A Review of State of the Art

Makki, Sara; Haque, Rafiqul; Taher, Yéhia; Assaghir, Zainab; Ditzler, Gregory; Hacid, Mohand-Saïd; Zeineddine, Hassan

doi:10.1109/fas-w.2017.154

Cited by 12 publications

(10 citation statements)

References 24 publications

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“…1). Once the confusion matrix is obtained, the True Positive Rate (TPR) for both majority and minority classes shall be calculated using (5) and (6) (Line 20 -22, Algo. 1).…”

Section: B Tackling the Problem Using Mcsmentioning

confidence: 99%

“…The experiments were evaluated using TPR. The TPR for both majority and minority classes can be calculated using (5) and (6).…”

Section: B Tackling the Problem Using Mcsmentioning

confidence: 99%

“…Researchers have been vigorously finding ways to tackle both issues, including data mining. Data mining is not an option or a trend, but more of a necessity that the banking sector should invest in [5], [6]. However, banking data such as credit card fraud and default payment are quite of a challenge to data mining researchers.…”

Section: Introductionmentioning

confidence: 99%

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A Multiple Classifiers System for Anomaly Detection in Credit Card Data With Unbalanced and Overlapped Classes

Kalid

Tong

et al. 2020

IEEE Access

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Frauds and default payments are two major anomalies in credit card transactions. Researchers have been vigorously finding solutions to tackle them and one of the solutions is to use data mining approaches. However, the collected credit card data can be quite a challenge for researchers. This is because of the data characteristics that contain: (i) unbalanced class distribution, and (ii) overlapping of class samples. Both characteristics generally cause low detection rates for the anomalies that are minorities in the data. On top of that, the weakness of general learning algorithms contributes to the difficulties of classifying the anomalies as the algorithms generally bias towards the majority class samples. In this study, we used a Multiple Classifiers System (MCS) on these two data sets: (i) credit card frauds (CCF), and (ii) credit card default payments (CCDP). The MCS employs a sequential decision combination strategy to produce accurate anomaly detection. Our empirical studies show that the MCS outperforms the existing research, particularly in detecting the anomalies that are minorities in these two credit card data sets. INDEX TERMS Anomaly detection, credit card, multiple classifiers.

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“…1). Once the confusion matrix is obtained, the True Positive Rate (TPR) for both majority and minority classes shall be calculated using (5) and (6) (Line 20 -22, Algo. 1).…”

Section: B Tackling the Problem Using Mcsmentioning

confidence: 99%

“…The experiments were evaluated using TPR. The TPR for both majority and minority classes can be calculated using (5) and (6).…”

Section: B Tackling the Problem Using Mcsmentioning

confidence: 99%

See 1 more Smart Citation

A Multiple Classifiers System for Anomaly Detection in Credit Card Data With Unbalanced and Overlapped Classes

Kalid

Tong

et al. 2020

IEEE Access

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“…T HE imbalanced data are quite common in many real applications. This type of data has been widely observed in many fields of recent scientific discoveries such as fraud detection and anomaly detection in general [1]. To date, the classification of class imbalance data constitutes a relatively new challenge, especially in a binary classification problem, where the positive or the negative class outnumbers the other class [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Minimizing the Overlapping Degree to Improve Class-Imbalanced Learning Under Sparse Feature Selection: Application to Fraud Detection

et al. 2021

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In recent years, the classification of class-imbalanced data has obtained increasing attention across different scientific areas such as fraud detection, metabolomics, Cancer diagnosis, etc. This interest comes after proving the negative effect of overlapping on the performance of class-imbalanced learning. Based on augmented R-value, our proposed strategy aims to select features that make data achieve the minimal overlap degree, so improving the performance of classification as well. In this context, we present three feature selection algorithms RONS (Reduce Overlapping with No-sampling), ROS (Reduce Overlapping with SMOTE), and ROA (Reduce Overlapping with ADASYN), which are built through sparse feature selection to minimize the overlapping and perform binary classification. Also, a re-sampling process has been included in both ROS and ROA. Simulation results show that our proposed algorithms as feature selection methods manage the variation of false discovery rate during the selection of main features for the process modeling. For the experiment, four credit card datasets have been selected to test the performance of our algorithms. Using F-measure and Gmean evaluation metrics, the results reveal that our proposed algorithms are considerably recommended compared with classical feature selection methods. Besides, this effective feature selection strategy can be extended as an alternative to deal with class-imbalanced learning problems that involve overlapping.

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“…Issues and challenges highlighted in [4] [5] paper are generally concept drift or dynamic fraud patterns, overlapping data, capability to support real-time detection requirements, skewed distribution, integrating a vast amount of data, and data quality-related issues. Concept drift is the challenge to deal with sudden customer behavioral changes which could turn out to be a false positive outcome.…”

Section: Introductionmentioning

confidence: 99%

Fraud Detection in Shipping Industry using K-NN Algorithm

Ganesan¹,

Moamin²

2021

IJACSA

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The shipment industry is going through tremendous growth in volume thanks to technological innovation in e-commerce and global trade liberalization. Volume growth also means a rise in fraud cases involving smuggling and false declaration of shipments. Shipping companies and customs are mostly relying on routine random inspection thus finding fraud is often by chance. As the volume increases dramatically it would no longer be sustainable and effective for both shipment companies and customs to pursue traditional fraud detection strategies. Other related papers on this area have proven that intelligent data-driven fraud detection is proven to be far more effective than routine inspections. However, the challenge in data-driven detection is its effectiveness are often reliant on the availability of data and the various fraud mechanism used by fraudsters to commit shipment related fraud. As such in this paper, we review and subsequently identify the most optimized approaches and algorithms to detect fraud effectively within the shipping industry. We also identify factors that influence fraud activity, review existing fraud detection models, develop the detection framework and implement the framework using the Rapidminer tool.

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Fraud Analysis Approaches in the Age of Big Data - A Review of State of the Art

Cited by 12 publications

References 24 publications

A Multiple Classifiers System for Anomaly Detection in Credit Card Data With Unbalanced and Overlapped Classes

A Multiple Classifiers System for Anomaly Detection in Credit Card Data With Unbalanced and Overlapped Classes

Minimizing the Overlapping Degree to Improve Class-Imbalanced Learning Under Sparse Feature Selection: Application to Fraud Detection

Fraud Detection in Shipping Industry using K-NN Algorithm

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