Improving a credit card fraud detection system using genetic algorithm

Özçelik, Merve; Duman, Ekrem; Işık, Mine; Cevik, Tugba

doi:10.1109/icnit.2010.5508478

Cited by 27 publications

(8 citation statements)

References 8 publications

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“…In [4] many models are implemented for fraud detection. In every model different algorithm are used.…”

Section: Related Workmentioning

confidence: 99%

Credit Card Fraud Detection using Machine Learning Algorithms

S¹

2020

IJERT

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Now a days Using technology like phishing technique to do internet banking fraud means transferring and removing the money from banker account without the permission of the banker. credit card frauds are happening in large amount and also some banking companies and the companies giving service to banks are facing problem. In this project trying build the model which predict fraud and nonfraud transaction in best way using machine learning algorithms and neural networks. Objective of the project is to predict the fraud and fraud less transaction with respect to the time and amount of the transaction using classification machine learning algorithms and statistics and calculus (differentiation ,chain rule etc) and linear algebra in building of the complex machine learning models for prediction and understanding of the data set . we have achieved accuracy of 94.84% using logistic regression and 91.62% using naive Bayes and 92.88% using decision tree and we step into deep learning, we used ANN achieved better accuracy then all other algorithms of 98.69%.

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“…In [4] many models are implemented for fraud detection. In every model different algorithm are used.…”

Section: Related Workmentioning

confidence: 99%

Credit Card Fraud Detection using Machine Learning Algorithms

S¹

2020

IJERT

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“…This technique is the genetic algorithm. To improve the performance of an existing fraud detection system, researchers (Özçelik, Duman, Işik, & Çevik, 2010) used this algorithm. Researchers (Patel, Singh, & Engineering, 2013) also used this algorithm for the prevention of CCFs.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Credit Card Fraud Detection using Machine Learning: A Systematic Literature Review

Paruchuri

2017

ABC j. adv. res.

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Companies want to give more and more facilities to their customers. One of these facilities is the online mode of buying goods. The customers now can buy the required goods online but this is also an opportunity for criminals to do frauds. The criminals can theft the information of any cardholder and use it for online purchases until the cardholder contacts the bank to block the card. This paper shows the different algorithms of machine learning that are used for detecting this kind of transaction.

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“…The rule-based approach is successful for identifying fraudulent transactions that follow previously observed fraud patterns, but it lacks agility. Before a new rule is added to the existing rule set, a considerable number of fraudulent transactions matching that rule have typically already Gadi, Wang, & Lago, 2008aGadi, Wang, & Lago, 2008bPatil, Karad, Wadhai, Gokhale, & Halgaonkar, 2010Sherly & Nedunchezhian, 2010Bhattacharyya et al, 2011Sahin & Duman, 2011aAlowais & Soon, 2012 Neural networks (NN) Maes, Tuyls, Vanschoenwinkel, & Manderick, 1993Aleskerov, Freisleben, & Rao, 1997Gadi et al, 2008aGadi et al, 2008bSahin & Duman, 2011b Bayesian networks (BN) Maes et al, 1993Filippov, Mukhanov, & Shchukin, 2008Gadi et al, 2008aGadi et al, 2008b Naïve Bayes (NB) Filippov et al, 2008Gadi et al, 2008aGadi et al, 2008bAlowais & Soon, 2012 Support vector machines (SVM) Chen, Chen, Chien, & Yang, 2005Bhattacharyya et al, 2011Sahin & Duman, 2011aHejazi & Singh, 2012 Genetic algorithm (GA) Ma & Li, 2009Ozcelik, Isik, Duman, & Cevik, 2010Duman & Ozcelik, 2011 Artificial immune system (AIS) Gadi et al, 2008aGadi et al, 2008b Hidden Markov model (HMM) Bhusari & Patil, 2011Rani, Kumar, Mohan, & Shankar, 2011 occurred. A long delay is required before a rule can be added, during which time fraud strategies may change, making the rule obsolete (Krivko, 2010).…”

Section: Introductionmentioning

confidence: 99%

Hybrid approaches for detecting credit card fraud

Kultur

Caglayan

2016

Expert Systems

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As a natural consequence of the multibillion dollar annual losses incurred as a result of credit card fraud, banks attach great importance to credit card fraud detection. In this paper, we proposed the use of six known models, namely, decision tree, random forest, Bayesian network, Naïve Bayes, support vector machine, and K* models, to form an ensemble for the detection of credit card fraud. We focused on the voting mechanisms used by the ensemble and proposed optimistic, pessimistic, and weighted voting strategies. The proposed model is called optimistic, pessimistic, and weighted voting in an ensemble of models. A dataset of real credit card transactions from a leading bank in Turkey was used to evaluate the performance of optimistic, pessimistic, and weighted voting in an ensemble of models. The results showed that the optimistic voting strategy enables the detection of 31.59% of fraudulent transactions with a false alarm rate of only 0.10%, the pessimistic voting strategy detects 93.92% of fraudulent transactions with a false alarm rate of 13.72%, and the weighted voting strategy detects 64.02% of fraudulent transactions with a false alarm rate of 0.75%. Banks can choose among these voting mechanisms depending on their preferred strategies for fraud detection and desired false alarm rates.

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Improving a credit card fraud detection system using genetic algorithm

Cited by 27 publications

References 8 publications

Credit Card Fraud Detection using Machine Learning Algorithms

Credit Card Fraud Detection using Machine Learning Algorithms

Credit Card Fraud Detection using Machine Learning: A Systematic Literature Review

Hybrid approaches for detecting credit card fraud

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