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Credit card fraud detection Using Improved Deep Learning Models

Computers, Materials & Continua 2024年第1期78卷 1049-1069页

作者： Sumaya S.Sulaiman Ibraheem Nadher Sarab M.Hameed Computer Science Department Al-Mustansiriya UniversityBaghdad10001Iraq Computer Science Department University of BaghdadBaghdad10001Iraq Faculty of Basic Education Al-Mustansiriya UniversityBaghdad10001Iraq

fraud of credit cards is a major issue for financial organizations and *** fraudulent actions become more complex,a demand for better fraud detection systems is *** learning approaches have shown promise in several fields,including detecting credit card ***,the efficacy of these models is heavily dependent on the careful selection of appropriate *** paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data,thereby improving fraud *** deep learning models:AutoEncoder(AE),Convolution Neural Network(CNN),and Long Short-Term Memory(LSTM)are proposed to investigate how hyperparameter adjustment impacts the efficacy of deep learning models used to identify credit card *** experiments conducted on a European credit card fraud dataset using different hyperparameters and three deep learning models demonstrate that the proposed models achieve a tradeoff between detection rate and precision,leading these models to be effective in accurately predicting credit card *** results demonstrate that LSTM significantly outperformed AE and CNN in terms of accuracy(99.2%),detection rate(93.3%),and area under the curve(96.3%).These proposed models have surpassed those of existing studies and are expected to make a significant contribution to the field of credit card fraud detection.

关键词： card fraud detection hyperparameter tuning deep learning autoencoder convolution neural network long short-term memory resampling

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