Ensemble learning for credit card fraud detection

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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“…Reference [61] used AdaBoost on a credit card fraud data set. The data set is highly unbalanced with only 0.173% fraud transaction.…”

Section: ) Sequential Combinationmentioning

confidence: 99%

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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“…The Nilson report found that by 2023, the worldwide fraud loss is expected to reach $35.67 billion annually [3]. Fraud prevention and fraud detection are two main ways to combat credit card fraud [4]. Fraud prevention consists of a series of rules, procedures, and protocols.…”

Section: Introductionmentioning

confidence: 99%

“…Commonly used technologies in fraud prevention include secure payment gateways, intrusion detection systems, and firewalls [5]. Fraud detection takes place after the fraud prevention mechanism has been breached [4], which means that fraud detection is the last line of defense to ensure the security of credit card transactions. Banks have to invest considerable money to optimize their fraud detection system [6], due to the need to protect cardholder's funds and their own business reputation.…”

Section: Introductionmentioning

confidence: 99%

“…Armel and Zaidouni [30] compared and analyzed the effectiveness of simple anomaly detection using DT, RF, and Naive Bayes (NB) in CCFD through a series of experiments. Sohony et al [4] found that RF enables higher accuracy in predicting legal transaction instances and a Feedforward Neural Network (FNN) achieves higher accuracy in predicting instances of fraudulent transactions. Consequently, they proposed an ensemble learning model based on RF and FNN.…”

Section: Introductionmentioning

confidence: 99%

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A Heterogeneous Ensemble Learning Model Based on Data Distribution for Credit Card Fraud Detection

Xie

Gao

et al. 2021

Wireless Communications and Mobile Computing

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Credit card fraud detection (CCFD) is important for protecting the cardholder’s property and the reputation of banks. Class imbalance in credit card transaction data is a primary factor affecting the classification performance of current detection models. However, prior approaches are aimed at improving the prediction accuracy of the minority class samples (fraudulent transactions), but this usually leads to a significant drop in the model’s predictive performance for the majority class samples (legal transactions), which greatly increases the investigation cost for banks. In this paper, we propose a heterogeneous ensemble learning model based on data distribution (HELMDD) to deal with imbalanced data in CCFD. We validate the effectiveness of HELMDD on two real credit card datasets. The experimental results demonstrate that compared with current state-of-the-art models, HELMDD has the best comprehensive performance. HELMDD not only achieves good recall rates for both the minority class and the majority class but also increases the savings rate for banks to 0.8623 and 0.6696, respectively.

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“…To resist these types of fraud feasible solutions are prevention and detection techniques. To achieve better performance they implemented ensemble machine learning technique, in their examination normal instances are predicted by random forest and abnormal instances were detected by neural network [21]. Abakarim Y et al, (2018) proposed a deep learning neural network algorithm to detect credit card frauds.…”

Section: Introductionmentioning

confidence: 99%

Authentication using Robust Primary PIN (Personal Identification Number), Multifactor Authentication for Credit Card Swipe and Online Transactions Security

Vaithyasubramanian¹

2020

IJACSA

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In view of effectiveness, ease of access and profitability, the advancement in e-Commerce is an immense step in forward. The development is went with further unusual vulnerabilities to worry. The significant issue all through the world in credit card management is credit card fraud. Because of extortion and fraudsters persistently look for better approaches to confer unlawful activities the organizations, users and establishment finds tremendous loss yearly. In the common Credit Card extortion process, fraudulent transaction will be distinguished only after the transaction is finished. In recent Studies, the security of Credit Card Transaction from unauthorized admittance or usage are addressed by diverse access control methods. This paper illustrates a new scheme of Authentication using Primary PIN and Multifactor authentication to secure credit card transactions.

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Ensemble learning for credit card fraud detection

Cited by 66 publications

References 15 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

A Heterogeneous Ensemble Learning Model Based on Data Distribution for Credit Card Fraud Detection

Authentication using Robust Primary PIN (Personal Identification Number), Multifactor Authentication for Credit Card Swipe and Online Transactions Security

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