Credit Card Fraud Detection by Modelling Behaviour Pattern using Hybrid Ensemble Model

Karthik, V; Mishra, Abinash; Reddy, U. Srinivasulu

doi:10.1007/s13369-021-06147-9

Cited by 24 publications

(13 citation statements)

References 32 publications

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“…Karthik et al [154] proposed a hybrid ensemble method for credit card fraud detection. The proposed method combined boosting and bagging techniques to obtain a robust model.…”

Section: Ensemble Learning Applications In Recent Literaturementioning

confidence: 99%

A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

2022

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Ensemble learning techniques have achieved state-of-the-art performance in diverse machine learning applications by combining the predictions from two or more base models. This paper presents a concise overview of ensemble learning, covering the three main ensemble methods: bagging, boosting, and stacking, their early development to the recent state-of-the-art algorithms. The study focuses on the widely used ensemble algorithms, including random forest, adaptive boosting (AdaBoost), gradient boosting, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost). An attempt is made to concisely cover their mathematical and algorithmic representations, which is lacking in the existing literature and would be beneficial to machine learning researchers and practitioners.

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“…Karthik et al [154] proposed a hybrid ensemble method for credit card fraud detection. The proposed method combined boosting and bagging techniques to obtain a robust model.…”

Section: Ensemble Learning Applications In Recent Literaturementioning

confidence: 99%

A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

2022

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“…In [14], the authors have focused on a new ensemble learning algorithm that combined bagging and boosting. As a result, detecting credit card fraud is a difficult task.…”

Section: Related Workmentioning

confidence: 99%

“…Bootstrap aggregation, sometimes known as "bagging," is a common strategy applied in ensemble learning-based models that integrate both classification and regression techniques, hence increasing accuracy and other associated metrics. The principle of bagging is the combination of weak learners with a strong learner [14]. For our experimentation, we implemented decision tree-based bagging classifiers such as random forest-based classifiers.…”

Section: B Bagging-based Ensemble Learningmentioning

confidence: 99%

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Detection of Credit Card Fraud using a Hybrid Ensemble Model

Saraf¹,

Phakatkar²

2022

IJACSA

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The rising number of credit card frauds presents a significant challenge for the banking industry. Many businesses and financial institutions suffer huge losses because card users are reluctant to use their cards. A primary goal of fraud detection is to identify prior transaction patterns to detect future fraud. In this paper, a hybrid ensemble model is proposed to combine bagging and boosting techniques to distinguish between fraudulent and legitimate transactions. During the experimentation two datasets are used; the European credit card dataset and the credit card stimulation dataset which are highly imbalanced. The oversampling method is used to balance both datasets. To overcome the problem of unbalanced data oversampling method is used. The model is trained to predict output results by combining random forest with Adaboost. The proposed model provides 98.27 % area under curve score on the European credit cards dataset and the stimulation credit card dataset gives 99.3 % area under curve score.

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“…The experimental results also showed that majority voting method has good accuracy in detecting credit card fraud cases. Karthik et al [13] constructed a new model for credit card fraud detection by building a hybrid model of bagging and boosting integrated classifier, fusing the key features of both techniques. However, these studies ignore the fact that the traditional Adaboost algorithm is prone to overfitting when there are noisy samples in the sample set, which makes the classification effect poor.…”

Section: Introductionmentioning

confidence: 99%

A Credit Card Fraud Model Prediction Method Based on Penalty Factor Optimization AWTadaboost

Wang¹,

Chen²,

Fu³

et al. 2023

Computers, Materials &Amp; Continua

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With the popularity of online payment, how to perform credit card fraud detection more accurately has also become a hot issue. And with the emergence of the adaptive boosting algorithm (Adaboost), credit card fraud detection has started to use this method in large numbers, but the traditional Adaboost is prone to overfitting in the presence of noisy samples. Therefore, in order to alleviate this phenomenon, this paper proposes a new idea: using the number of consecutive sample misclassifications to determine the noisy samples, while constructing a penalty factor to reconstruct the sample weight assignment. Firstly, the theoretical analysis shows that the traditional Adaboost method is overfitting in a noisy training set, which leads to the degradation of classification accuracy. To this end, the penalty factor constructed by the number of consecutive misclassifications of samples is used to reconstruct the sample weight assignment to prevent the classifier from over-focusing on noisy samples, and its reasonableness is demonstrated. Then, by comparing the penalty strength of the three different penalty factors proposed in this paper, a more reasonable penalty factor is selected. Meanwhile, in order to make the constructed model more in line with the actual requirements on training time consumption, the Adaboost algorithm with adaptive weight trimming (AWTAdaboost) is used in this paper, so the penalty factor-based AWTAdaboost (PF_AWTAdaboost) is finally obtained. Finally, PF_AWTAdaboost is experimentally validated against other traditional machine learning algorithms on credit card fraud datasets and other datasets. The results show that the PF_AWTAdaboost method has better performance, including detection accuracy, model recall and robustness, than other methods on the credit card fraud dataset. And the PF_AWTAdaboost method also shows excellent generalization performance on other datasets. From the experimental results, it is shown that the PF_AWTAdaboost algorithm has better classification performance.

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Credit Card Fraud Detection by Modelling Behaviour Pattern using Hybrid Ensemble Model

Cited by 24 publications

References 32 publications

A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

Detection of Credit Card Fraud using a Hybrid Ensemble Model

A Credit Card Fraud Model Prediction Method Based on Penalty Factor Optimization AWTadaboost

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