Detection of credit card fraudwith optimized deep neuralnetworkin balanced data condition

• Autorzy: Shome Nirupam , Sarkar Devran Dey , Kashyap Richik , Laskar Rabul Hussain

Identyfikatory

DOI

10.7494/csci.2024.25.2.5967

• Języki publikacji: EN

Abstrakty

EN

Due to the huge number of financial transactions, it is almost impossible forhumans to manually detect fraudulent transactions. In previous work, thedatasets are not balanced and the models suffer from overfitting problems. Inthis paper, we tried to overcome the problems by tuning hyperparameters andbalancing the dataset with a hybrid approach using under-sampling and over-sampling techniques. In this study, we have observed that these modificationsare effective in getting better performance in comparison to the existing models.The MCC score is considered an important parameter in binary classificationsince it ensures the correct prediction of the majority of positive data instancesand negative data instances. So, we emphasize on MCC score and our methodachieved an MCC score of 97.09%, which is far more (16 % approx.) than otherstate-of-the-art methods. In terms of other performance metrics, the result ofour proposed model has also improved significantly.

Słowa kluczowe

EN

credit card; raud detection; deep learning; fraud transactions

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2024
• Tom: T. 25 (2)
• Strony: 253--276
• Opis fizyczny: Bibliogr. 30 poz., rys., tab., wykr.

Twórcy

autor

Shome Nirupam

• Assam University, Department of Electronics and Communication Engineering, Silchar788011, India

autor

Sarkar Devran Dey

• Assam University, Department of Electronics and Communication Engineering, Silchar788011, India

autor

Kashyap Richik

• Assam University, Department of Electronics and Communication Engineering, Silchar788011, India

autor

Laskar Rabul Hussain

• National Institute of Technology, Department of Electronics and Communication Engineering,Silchar, 788010, India

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).