Cluster Analysis and Artificial Neural Networks: A Case Study in Credit Card Fraud Detection

Carneiro, Emanuel Mineda; Dias, Luiz Alberto Vieira; Cunha, Adilson Marques da; Mialaret, Lineu Fernando Stege

doi:10.1109/itng.2015.25

Cited by 33 publications

(10 citation statements)

References 8 publications

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“…We select two standard classification algorithms, SVM [14] and MLP-based ANN [6], [26] as well as the ensemble classification algorithm Random Forest [8] to develop the fraud classifiers by learning from the imbalanced and balanced SB datasets.…”

Section: Classifier Optimization and Evaluationmentioning

confidence: 99%

Detection of Auction Fraud in Commercial Sites

Anowar

Sadaoui

2020

J. theor. appl. electron. commer. res.

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Online auctions have become one of the most convenient ways to commit fraud due to a large amount of money being traded every day. Shill bidding is the predominant form of auction fraud, and it is also the most difficult to detect because it so closely resembles normal bidding behavior. Furthermore, shill bidding does not leave behind any apparent evidence, and it is relatively easy to use to cheat innocent buyers. Our goal is to develop a classification model that is capable of efficiently differentiating between legitimate bidders and shill bidders. For our study, we employ an actual training dataset, but the data are unlabeled. First, we properly label the shill bidding samples by combining a robust hierarchical clustering technique and a semi-automated labeling approach. Since shill bidding datasets are imbalanced, we assess advanced over-sampling, under-sampling and hybrid-sampling methods and compare their performances based on several classification algorithms. The optimal shill bidding classifier displays high detection and low misclassification rates of fraudulent activities.

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Section: Classifier Optimization and Evaluationmentioning

confidence: 99%

Detection of Auction Fraud in Commercial Sites

Anowar

Sadaoui

2020

J. theor. appl. electron. commer. res.

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“…There are several data mining techniques suggested for fraud detection [8][6] [1][11]. Artificial Intelligence, Neural networks, genetic programming, Support Vector machine, Decision tree.…”

Section: Related Workmentioning

confidence: 99%

Database Implementation and Testing of Dynamic Credit Card Fraud Detection System

Jog¹,

Chandavale²

2017

IJCA

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Credit card frauds are increasing with the increase in use of plastic money. These frauds include the transactions done either by stealing the physical card or using card data such as card number, expiry date and pin number. There is a need to recognize customer spending pattern and apply validations for incoming transaction. Suspicious transactions can go under rigorous security checks. This paper describes the database implementation of credit card fraud detection system which is adaptive to concept drift environment. The system is designed using PL-SQL stored procedures and JAVA. The validation procedure and testing results are included in this paper.

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“…having better fitness value. c. Hidden Markov Model (HMM): A double embedded stochastic process using which highly complicated stochastic processes can be generated is known as a hidden Markov model [9]. A Markov process that has unobserved states is assumed to be available within the underlying system.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Classification Method for Credit Card Fraud Detection

Kaur*¹,

Kalra²

2019

IJRTE

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Credit card frauds are on the rise and are getting smarter with the passage of time. Usually, fraudulent transactions are conducted by stealing the credit card. When the loss of the card is not noticed by the cardholder, a huge loss can be faced by the credit card company. In the existing work, it has been found that the researchers have utilized Voting based method to identify credit card frauds. The problem with voting based method is that they are more complex and more time consuming. In this research work, a hybrid approach based on KNN and Naive Bayes for the detection of credit card frauds. KNN will be used as the base classifier and it will return predicted result. The predicted result will be provided as input to the Naive Bayes classifier which will generate the final result. The proposed model will be compared with existing techniques and the results are analyzed in terms of recall, precision, accuracy and execution time.

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Cluster Analysis and Artificial Neural Networks: A Case Study in Credit Card Fraud Detection

Cited by 33 publications

References 8 publications

Detection of Auction Fraud in Commercial Sites

Detection of Auction Fraud in Commercial Sites

Database Implementation and Testing of Dynamic Credit Card Fraud Detection System

Ensemble Classification Method for Credit Card Fraud Detection

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