A Cost-sensitive weighted Random Forest Technique for Credit Card Fraud Detection

Devi, Debashree; Biswas, Saroj Kumar; Purkayastha, Biswajit

doi:10.1109/icccnt45670.2019.8944885

Cited by 25 publications

(12 citation statements)

References 11 publications

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“…Some of interesting approaches in the past were meta-classifier based fraud detection [23] , fraud detection based on behavior [16] , machine learning models [1], [22], [25] , data mining approaches [4], [25] , neural classification [9] , game-theory approach [21] web services based detection [24] , Hidden Markov Model [11], [14] , Predictive Analysis [19] and so on. In addition, applying some of the most robust classification algorithms [10] such as SVM [27] and ensemble algorithms [20] such as Random Forest [3], [15], [17] and Adaboost [13] was also preferred by a lot of researchers. Especially with voluminous data generated these days almost any algorithm which failed in the past could shine and the frauds of course shine accordingly.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Credit Card Fraud detection using Machine Learning models on balanced and imbalanced datasets

2021

IJETER

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With the advent of modern transaction technology, many are using online transactions to transfer money from one person to another. Credit Card Fraud, a rising problem in the financial department goes unnoticed most of the time. A lot of research is going on in this area.The Credit Card Fraud Detection project is developed to spot whether a new transaction is fraudulent or not with the knowledge of previousdata. We use various predictive models to ascertain how accurate they are in predicting whether a transaction is abnormalor regular. Techniques like Decision Tree, Logistic Regression, SVMand Naïve Bayes are the classification algorithms to detect non-fraud and fraud transactions.

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Section: Related Workmentioning

confidence: 99%

Analysis of Credit Card Fraud detection using Machine Learning models on balanced and imbalanced datasets

2021

IJETER

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“…The advantage of RF is its computational efficiency, as each single tree is built independently of each other [14]. Furthermore, [15] emphasize the ability of RF to deal with unbalanced datasets, the common issue related to the dataset with fraudulent credit card transactions. Unbalanced datasets are dealt with by incorporating the highly differenced misclassification costs of credit card detection and implementation of RF bagging ensemble learning model.…”

Section: B Random Forest Classifiermentioning

confidence: 99%

“…Next, these distances are arranged in an increasing order [18]. The performance of the KNN algorithm is determined by the following three main factors: 1) the distance used for the location of the nearest neighbours; 2) the distance rule used to deliver a classification from the nearest neighbour; and 3) the k number of neighbours used for classification of the outcome variable [15]. When k = 1, the data point is assigned to the class of its nearest neighbour data point.…”

Section: K-nearest Neighbourmentioning

confidence: 99%

“…Despite the existing efforts of detection methods using machine learning algorithms, an efficient model with the ability to balance the unbalanced datasets is still a lacking. In the same way, many of the researched sources use the dataset which is not available to the public [15], [35]- [36]. Thus this paper emphasises the need for more open research in the context of online fraud detection, particularly when with unbalanced dataset that characterises ambiguity.…”

Section: G Summarymentioning

confidence: 99%

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Efficient Resampling for Fraud Detection During Anonymised Credit Card Transactions with Unbalanced Datasets

Mrozek

Panneerselvam

Bagdasar

2020

2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC)

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The rapid growth of e-commerce and online shopping have resulted in an unprecedented increase in the amount of money that is annually lost to credit card fraudsters. In an attempt to address credit card fraud, researchers are leveraging the application of various machine learning techniques for efficiently detecting and preventing fraudulent credit card transactions. One of the prevalent common issues around the analytics of credit card transactions is the highly unbalanced nature of the datasets, which is frequently associated with the binary classification problems. This paper intends to review, analyse and implement a selection of notable machine learning algorithms such as Logistic Regression, Random Forest, K-Nearest Neighbours and Stochastic Gradient Descent, with the motivation of empirically evaluating their efficiencies in handling unbalanced datasets whilst detecting credit card fraud transactions. A publicly available dataset comprising 284807 transactions of European cardholders is analysed and trained with the studied machine learning techniques to detect fraudulent transactions. Furthermore, this paper also evaluates the incorporation of two notable resampling methods, namely Random Under-sampling and Synthetic Majority Oversampling Techniques (SMOTE) in the aforementioned algorithms, in order to analyse their efficiency in handling unbalanced datasets. The proposed resampling methods significantly increased the detection ability, the most successful technique of combination of Random Forest with Random Under-sampling achieved the recall score of 100% in contrast to the recall score 77% of model without resampling technique. The key contribution of this paper is the postulation of efficient machine learning algorithms together with suitable resampling methods, suitable for credit card fraud detection with unbalanced dataset.

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“…More and more methods and algorithms for anti-fraud detection have been proposed. Devi et al 1 introduce a cost-sensitive weighted random forest algorithm to detect credit card fraud. And some scholars also propose deep learning method to detect abnormal transactions.…”

Section: Introductionmentioning

confidence: 99%

A generative adversarial network–based method for generating negative financial samples

Zhang

Yang

Chen

et al. 2020

International Journal of Distributed Sensor Networks

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In financial anti-fraud field, negative samples are small and sparse with serious sample imbalanced problem. Generating negative samples consistent with original data to naturally solve imbalanced problem is a serious problem. This article proposes a new method to solve this problem. We introduce a new generation model, combined Generative Adversarial Network with Long Short-Term Memory network for one-dimensional negative financial samples. The characteristic association between transaction sequences can be learned by long short-term memory layer, and the generator covers real data distribution by the adversarial discriminator with time-sequence. Mapping data distribution to feature space is a common evaluation method of synthetic data; however, relationships between data attributes have been ignored in online transactions. We define a comprehensive evaluation method to evaluate the validity of generated samples from data distribution and attribute characteristics. Experimental results on real bank B2B transaction data show that the proposed model has higher overall ratings, which is 10% higher than traditional generation models. Finally, well-trained model is used to generate negative samples and form new dataset. The classification results on new datasets show that precision and recall are all higher than baseline models. Our work has a certain practical value and provides a new idea to solve imbalanced problem in whatever fields.

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A Cost-sensitive weighted Random Forest Technique for Credit Card Fraud Detection

Cited by 25 publications

References 11 publications

Analysis of Credit Card Fraud detection using Machine Learning models on balanced and imbalanced datasets

Analysis of Credit Card Fraud detection using Machine Learning models on balanced and imbalanced datasets

Efficient Resampling for Fraud Detection During Anonymised Credit Card Transactions with Unbalanced Datasets

A generative adversarial network–based method for generating negative financial samples

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