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Customer Behavior Analysis to Improve Detection of Fraudulent ‎Transactions using Deep Learning

عنوان مقاله: Customer Behavior Analysis to Improve Detection of Fraudulent ‎Transactions using Deep Learning
شناسه ملی مقاله: JR_JADM-10-1_008
منتشر شده در در سال 1401
مشخصات نویسندگان مقاله:

F. Baratzadeh - Department of Computer Engineering, Alzahra University, Tehran, Iran.
Seyed M. H. Hasheminejad - Department of Computer Engineering, Alzahra University, Tehran, Iran.

خلاصه مقاله:
With the advancement of technology, the daily use of bank credit cards has been increasing exponentially. Therefore, the fraudulent use of credit cards by others as one of the new crimes is also growing fast. For this reason, detecting and preventing these attacks has become an active area of study. This article discusses the challenges of detecting fraudulent banking transactions and presents solutions based on deep learning. Transactions are examined and compared with other traditional models in fraud detection. According to the results obtained, optimal performance is related to the combined model of deep convolutional networks and short-term memory, which is trained using the aggregated data received from the generative adversarial network. This paper intends to produce sensible data to address the unequal class distribution problem, which is far more effective than traditional methods. Also, it uses the strengths of the two approaches by combining deep convolutional network and Long Short Term Memory network to improve performance. Due to the inefficiency of evaluation criteria such as accuracy in this application, the measure of distance score and the equal error rate has been used to evaluate models more transparent and more precise. Traditional methods were compared to the proposed approach to evaluate the efficiency of the experiment.

کلمات کلیدی:
Bank Transaction Fraud, Equal Error Rate Criterion, Adversarial Neural Network, deep learning, fraud detection

صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/1424965/