Credit card fraud detection using ensemble data mining methods

Bakhtiari, Saeid; Nasiri, Zahra; Vahidi, Javad

doi:10.1007/s11042-023-14698-2

Cited by 16 publications

(5 citation statements)

References 20 publications

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“…The study [13] trains a set of 100 hyperparameters of LGBM and achieves a TPR result of about 60%. The study [15] trains a set of 25 LGBM and achieves a TPR result of about 80%. Also, the study [14] utilizes ensemble learning algorithms to achieve a TPR result of 90%.…”

Section: Comparison With State-of-the-art Methods In New Bank Account...mentioning

confidence: 99%

“…The model is put up against other classi ers like NB, SVM, and ANN and is discovered to offer effective results with high accuracy. The study [24][25][26] suggests using Bayesian optimization and class weight-tuning to identify fraudulent features in credit card transactions using gradient boosting and category boosting as an ensemble approach, the authors found Bayesian optimization to be e cient in improving computational time and performance. Additionally, deep learning [24] is used to optimize the hyperparameters and get better outcomes.…”

Section: Machine Learning Algorithms For Fraud Detectionmentioning

confidence: 99%

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Modeling the Dynamic Behaviors of Bank Account Fraudsters Using Combined Simultaneous Game Theory with Neural Networks

USMAN,

Abdullahi,

Ran

et al. 2024

Preprint

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New bank account fraud is a significant problem causing financial losses in banking and finance. Existing statistical and machine-learning methods were used to detect fraud thereby preventing financial losses. However, most studies do not consider the dynamic behavior of fraudsters and often produce a high False Positive Rate (FPR). This study proposes the detection of new bank account fraud in the context of simultaneous game theory (SGT) with Neural Networks, the SGT involves two players, a fraudster, and bank officials attacking each other through Bayesian probability in a zero-sum. The influence of outliers within the SGT was tackled by adding a context feature for effective simulation of the dynamic behavior of fraudsters. The Neural Networks layer uses the simulated features for fraud context learning. The study is validated using Bank Account Fraud (BAF) Dataset on different machine-learning models. The Radial Basis Function Networks achieved FPR of 0.0% and 8.3% for fraud and non-fraud classes, respectively, while achieving True Positive Rate (TPR) of 91.7% and 100.0% for fraud and non-fraud classes, respectively. An improved Radial Basis Function Networks detect fraud by revealing fraudulent patterns and dynamic behaviors in higher dimensional data. The findings will enhance fraud detection and reduce customer attrition.

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Section: Comparison With State-of-the-art Methods In New Bank Account...mentioning

confidence: 99%

Section: Machine Learning Algorithms For Fraud Detectionmentioning

confidence: 99%

Modeling the Dynamic Behaviors of Bank Account Fraudsters Using Combined Simultaneous Game Theory with Neural Networks

USMAN,

Abdullahi,

Ran

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Numerous well-liked classification methods have been developed over the past few decades, including support vector machine (SVM), naive Bayes (NB), k-nearest neighbor (KNN), random forest (RF), logistic regression (LR), and genetic algorithm (GA) algorithms for feature selection [19] have been proposed. Bakhtiari et al [20] provide ensemble learning techniques for identifying credit card fraud that incorporate gradient boosting (LightGBM and LiteMORT), and they combine these techniques by employing averaging techniques (simple and weighted averaging techniques) before being evaluated. By combining these approaches, error rates are decreased while efficiency and accuracy are improved.…”

Section: Related Workmentioning

confidence: 99%

A rule-based machine learning model for financial fraud detection

Islam,

Haque,

Rezaul Karim

2024

IJECE

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Financial fraud is a growing problem that poses a significant threat to the banking industry, the government sector, and the public. In response, financial institutions must continuously improve their fraud detection systems. Although preventative and security precautions are implemented to reduce financial fraud, criminals are constantly adapting and devising new ways to evade fraud prevention systems. The classification of transactions as legitimate or fraudulent poses a significant challenge for existing classification models due to highly imbalanced datasets. This research aims to develop rules to detect fraud transactions that do not involve any resampling technique. The effectiveness of the rule-based model (RBM) is assessed using a variety of metrics such as accuracy, specificity, precision, recall, confusion matrix, Matthew’s correlation coefficient (MCC), and receiver operating characteristic (ROC) values. The proposed rule-based model is compared to several existing machine learning models such as random forest (RF), decision tree (DT), multi-layer perceptron (MLP), k-nearest neighbor (KNN), naive Bayes (NB), and logistic regression (LR) using two benchmark datasets. The results of the experiment show that the proposed rule-based model beat the other methods, reaching accuracy and precision of 0.99 and 0.99, respectively.

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“…For this purpose, machine learning (ML), data mining, and traditional statistical classification approaches have all been used effectively [7]. Credit card fraud has been successfully detected using a variety of artificial neural network (ANN) models [8], which, by replicating the characteristics of interacting neurons, are renowned for their ability to simulate extremely non-linear and complex functions from the ground up [9]. Additionally, studies have been conducted utilizing explicit entity-relation networks to identify probable fraud.…”

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Networks-Based Novel Approach for Fraud Detection for the European Cardholders 2013 Dataset

Almarshad,

Gashgari,

Alzahrani

2023

IEEE Access

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Credit card use poses a significant security issue on a global scale, with rule-based algorithms and traditional anomaly detection being two of the most often used methods. However, they are resourceintensive, time-consuming, and erroneous. Given fewer instances than legal payments, the dataset imbalance has become a serious issue. On the other hand, the generative technique is considered an effective way to rebalance the imbalanced class issue, as this technique balances both minority and majority classes before the training. In a more recent period, GAN is considered one of the most popular data generative techniques, as it is used in significant data settings. Hence, the research under study explores a classification system to detect fraudulent credit card transactions that are being trained using the European Cardholders 2013 dataset. It has 30 features, 28 of which are hidden due to sensitive information. Fraud activity accounts for less than 1% of the entire transaction volume of $284807. Additionally, GANs is a generative model based on game theory, in which a generator G and a discriminator D compete with one another. The generator's goal is to make the discriminator uncertain. Distinguishing between instances from the generator and those from the original dataset is the discriminator's goal, and we can increase classifiers' discriminating strength by training GANs on a set of fraudulent credit card transactions. According to the outcome, our model outperformed the earlier experiments with an AUC score of 0.999. Additionally, it creates artificial data using GANs, enabling the production of a sizable volume of high-quality data. In terms of innovation and performance, this technique substantially improves over earlier research.

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Credit card fraud detection using ensemble data mining methods

Cited by 16 publications

References 20 publications

Modeling the Dynamic Behaviors of Bank Account Fraudsters Using Combined Simultaneous Game Theory with Neural Networks

Modeling the Dynamic Behaviors of Bank Account Fraudsters Using Combined Simultaneous Game Theory with Neural Networks

A rule-based machine learning model for financial fraud detection

Generative Adversarial Networks-Based Novel Approach for Fraud Detection for the European Cardholders 2013 Dataset

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